model_name stringclasses 20
values | predicted_class stringclasses 4
values | task_name stringlengths 13 44 | narration stringlengths 473 1.48k | values list | sign list | narrative_id int32 1 454 | unique_id int32 0 3.42k | classes_dict stringlengths 30 63 | narrative_questions list | feature_nums list | ft_num2name stringlengths 78 3.67k | old2new_ft_nums stringlengths 72 1.28k | old2new_classes stringlengths 24 48 | predicted_class_label stringlengths 2 23 | class2name stringlengths 25 85 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SVC | C1 | Health Care Services Satisfaction Prediction | The prediction probability associated with class C2 and class C1, respectively, is 35.34% and 64.66%. Based on these probabilities, the model labels the given case as C1 since it is the most probable class. According to the attribution analysis, the most relevant features considered by the model here are F16, F7, and F... | [
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"positive",
"positive",
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] | 208 | 445 | {'C2': '35.34%', 'C1': '64.66%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
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"F13",
"F2",
"F11",
"F9",
"F4",
"F10",
"F8",
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"F12"
] | {'F16': 'waiting rooms', 'F6': 'Hygiene and cleaning', 'F7': 'Specialists avaliable', 'F14': 'Quality\\/experience dr.', 'F3': 'Modern equipment', 'F1': 'Exact diagnosis', 'F13': 'hospital rooms quality', 'F2': 'Check up appointment', 'F11': 'avaliablity of drugs', 'F9': 'friendly health care workers', 'F4': 'Time wait... | {'F14': 'F16', 'F4': 'F6', 'F7': 'F7', 'F6': 'F14', 'F10': 'F3', 'F9': 'F1', 'F15': 'F13', 'F1': 'F2', 'F13': 'F11', 'F11': 'F9', 'F2': 'F4', 'F8': 'F10', 'F12': 'F8', 'F16': 'F5', 'F5': 'F15', 'F3': 'F12'} | {'C2': 'C2', 'C1': 'C1'} | Satisfied | {'C2': 'Dissatisfied', 'C1': 'Satisfied'} |
KNeighborsClassifier | C1 | Real Estate Investment | The classifier is very uncertain about the correct label for the case given. Regarding the classifier's decision, there is close to an even split on the probability of either of the possible labels is the correct label but the classifier chooses the label as C1. The prediction verdict above is attributed to the contri... | [
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] | 185 | 107 | {'C1': '50.00%', 'C2': '50.00%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F2 and F14.",
"Summarize the... | [
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"F12",
"F17",
"F9",
"F3",
"F18",
"F5",
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] | {'F2': 'Feature7', 'F14': 'Feature4', 'F13': 'Feature2', 'F10': 'Feature8', 'F19': 'Feature20', 'F16': 'Feature1', 'F11': 'Feature12', 'F6': 'Feature15', 'F20': 'Feature6', 'F7': 'Feature9', 'F8': 'Feature17', 'F12': 'Feature3', 'F17': 'Feature19', 'F9': 'Feature13', 'F3': 'Feature18', 'F18': 'Feature5', 'F5': 'Feature... | {'F11': 'F2', 'F9': 'F14', 'F1': 'F13', 'F3': 'F10', 'F20': 'F19', 'F7': 'F16', 'F15': 'F11', 'F4': 'F6', 'F10': 'F20', 'F12': 'F7', 'F6': 'F8', 'F8': 'F12', 'F5': 'F17', 'F16': 'F9', 'F19': 'F3', 'F2': 'F18', 'F14': 'F5', 'F18': 'F4', 'F13': 'F1', 'F17': 'F15'} | {'C2': 'C1', 'C1': 'C2'} | Ignore | {'C1': 'Ignore', 'C2': 'Invest'} |
DecisionTreeClassifier | C1 | Car Acceptability Valuation | The classification algorithm believes that C1 is the output label that was generated with 100% certainty and that C2 is unlikely to be the correct label in this case. According to the attribution investigations, the following input features are ranked from most relevant to least relevant: F6, F3, F5, F2, F1, and F4. As... | [
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"-0.09",
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] | [
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 18 | 309 | {'C1': '100.00%', 'C2': '0.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F6",
"F3",
"F5",
"F2",
"F1",
"F4"
] | {'F6': 'safety', 'F3': 'persons', 'F5': 'buying', 'F2': 'maint', 'F1': 'lug_boot', 'F4': 'doors'} | {'F6': 'F6', 'F4': 'F3', 'F1': 'F5', 'F2': 'F2', 'F5': 'F1', 'F3': 'F4'} | {'C2': 'C1', 'C1': 'C2'} | Unacceptable | {'C1': 'Unacceptable', 'C2': 'Acceptable'} |
RandomForestClassifier | C3 | Flight Price-Range Classification | Of the three possible labels, there is 100.0% confidence that C3 is the most probable label for the given case. The features that heavily influence the classification verdict presented here are F10, F5, and F2, and they have a very strong positive contribution, increasing the odds of the C3 prediction. Other features w... | [
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"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
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] | 114 | 237 | {'C3': '100.00%', 'C1': '0.00%', 'C2': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F2",
"F10",
"F5",
"F7",
"F9",
"F6",
"F8",
"F3",
"F4",
"F11",
"F12",
"F1"
] | {'F2': 'Duration_hours', 'F10': 'Airline', 'F5': 'Total_Stops', 'F7': 'Journey_day', 'F9': 'Source', 'F6': 'Destination', 'F8': 'Journey_month', 'F3': 'Dep_minute', 'F4': 'Arrival_minute', 'F11': 'Arrival_hour', 'F12': 'Duration_mins', 'F1': 'Dep_hour'} | {'F7': 'F2', 'F9': 'F10', 'F12': 'F5', 'F1': 'F7', 'F10': 'F9', 'F11': 'F6', 'F2': 'F8', 'F4': 'F3', 'F6': 'F4', 'F5': 'F11', 'F8': 'F12', 'F3': 'F1'} | {'C3': 'C3', 'C2': 'C1', 'C1': 'C2'} | Low | {'C3': 'Low', 'C1': 'Moderate', 'C2': 'High'} |
RandomForestClassifier | C2 | Used Cars Price-Range Prediction | Per the model, class C1 has a prediction probability of 10.50 percent, whereas class C2 has a predicted probability of 89.50 percent. As a result of the model, it can be determined that C2 is the most likely label for the given scenario. All of the input features are shown to contribute to the above conclusion, with F2... | [
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] | 259 | 334 | {'C1': '10.50%', 'C2': '89.50%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F6",
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"F1",
"F3",
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] | {'F6': 'Power', 'F2': 'car_age', 'F8': 'Transmission', 'F5': 'Fuel_Type', 'F1': 'Name', 'F3': 'Mileage', 'F4': 'Engine', 'F7': 'Owner_Type', 'F9': 'Kilometers_Driven', 'F10': 'Seats'} | {'F4': 'F6', 'F5': 'F2', 'F8': 'F8', 'F7': 'F5', 'F6': 'F1', 'F2': 'F3', 'F3': 'F4', 'F9': 'F7', 'F1': 'F9', 'F10': 'F10'} | {'C2': 'C1', 'C1': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
AdaBoostClassifier | C2 | Basketball Players Career Length Prediction | The classifier says that C2 is the most likely label for the provided data with relatively high confidence. It is crucial to remember, however, that there is a 21.80% possibility that it is C1. F12 and F10 are the major driving variables for the aforementioned classification or prediction choice. The remaining variable... | [
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"negative",
"positive",
"positive",
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"positive",
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"positive",
"negative",
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] | 256 | 337 | {'C2': '78.20%', 'C1': '21.80%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F12",
"F10",
"F5",
"F13",
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"F3",
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"F14",
"F6",
"F1",
"F9",
"F16",
"F15",
"F17",
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] | {'F12': 'GamesPlayed', 'F10': 'PointsPerGame', 'F5': 'Steals', 'F13': 'MinutesPlayed', 'F19': 'DefensiveRebounds', 'F3': 'Rebounds', 'F18': 'Blocks', 'F7': 'FreeThrowAttempt', 'F4': 'FieldGoalPercent', 'F14': 'FreeThrowMade', 'F6': 'OffensiveRebounds', 'F1': 'FieldGoalsMade', 'F9': '3PointAttempt', 'F16': 'FreeThrowPer... | {'F1': 'F12', 'F3': 'F10', 'F17': 'F5', 'F2': 'F13', 'F14': 'F19', 'F15': 'F3', 'F18': 'F18', 'F11': 'F7', 'F6': 'F4', 'F10': 'F14', 'F13': 'F6', 'F4': 'F1', 'F8': 'F9', 'F12': 'F16', 'F7': 'F15', 'F5': 'F17', 'F19': 'F2', 'F16': 'F11', 'F9': 'F8'} | {'C2': 'C2', 'C1': 'C1'} | More than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
RandomForestClassifier | C2 | Printer Sales | C2 has an 83.0% chance of being the correct label for the case under consideration, making C1 the least likely class with a predicted likelihood of 17.0%. F17, F21, and F24 features have a significant impact on class selection here while on the other hand, the remaining features are shown to have marginal to no contrib... | [
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"neg... | 240 | 323 | {'C2': '83.00%', 'C1': '17.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F17",
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"F6",
"F15",
"F5",
"F16",
"F13",
"F14",
"F12",
"F22",
"F3",
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] | {'F17': 'X8', 'F21': 'X24', 'F24': 'X1', 'F9': 'X2', 'F18': 'X10', 'F11': 'X15', 'F8': 'X25', 'F23': 'X23', 'F2': 'X18', 'F1': 'X4', 'F7': 'X7', 'F19': 'X17', 'F10': 'X3', 'F4': 'X22', 'F26': 'X5', 'F25': 'X9', 'F6': 'X12', 'F15': 'X19', 'F5': 'X11', 'F16': 'X16', 'F13': 'X14', 'F14': 'X21', 'F12': 'X20', 'F22': 'X13',... | {'F8': 'F17', 'F24': 'F21', 'F1': 'F24', 'F2': 'F9', 'F10': 'F18', 'F15': 'F11', 'F25': 'F8', 'F23': 'F23', 'F18': 'F2', 'F4': 'F1', 'F7': 'F7', 'F17': 'F19', 'F3': 'F10', 'F22': 'F4', 'F5': 'F26', 'F9': 'F25', 'F12': 'F6', 'F19': 'F15', 'F11': 'F5', 'F16': 'F16', 'F14': 'F13', 'F21': 'F14', 'F20': 'F12', 'F13': 'F22',... | {'C1': 'C2', 'C2': 'C1'} | Less | {'C2': 'Less', 'C1': 'More'} |
LogisticRegression | C2 | Music Concert Attendance | C2 is the label picked by the algorithm with about 82.06% certainty, since the prediction likelihood of C1 is only 17.94%. F13, F11, F20, and F15 all contribute significantly to the above classification output and among them, the features that support the most positive contribution to the C2 prediction are F15, F13, an... | [
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"positive",
"negative",
"positive",
"positive",
"negative",
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] | 46 | 295 | {'C1': '17.94%', 'C2': '82.06%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F11, F16 and F9) on the model’s prediction of C2.",
"Summarize the set of ... | [
"F15",
"F13",
"F20",
"F11",
"F16",
"F9",
"F18",
"F19",
"F5",
"F12",
"F6",
"F7",
"F4",
"F1",
"F10",
"F8",
"F14",
"F17",
"F3",
"F2"
] | {'F15': 'X11', 'F13': 'X1', 'F20': 'X13', 'F11': 'X3', 'F16': 'X8', 'F9': 'X6', 'F18': 'X2', 'F19': 'X9', 'F5': 'X17', 'F12': 'X10', 'F6': 'X4', 'F7': 'X14', 'F4': 'X20', 'F1': 'X18', 'F10': 'X19', 'F8': 'X7', 'F14': 'X12', 'F17': 'X15', 'F3': 'X16', 'F2': 'X5'} | {'F11': 'F15', 'F1': 'F13', 'F13': 'F20', 'F3': 'F11', 'F8': 'F16', 'F6': 'F9', 'F2': 'F18', 'F9': 'F19', 'F17': 'F5', 'F10': 'F12', 'F4': 'F6', 'F14': 'F7', 'F20': 'F4', 'F18': 'F1', 'F19': 'F10', 'F7': 'F8', 'F12': 'F14', 'F15': 'F17', 'F16': 'F3', 'F5': 'F2'} | {'C1': 'C1', 'C2': 'C2'} | > 10k | {'C1': '< 10k', 'C2': '> 10k'} |
LogisticRegression | C1 | Flight Price-Range Classification | The model is confident in its prediction, as it predicted class C1 with a likelihood of 90.48% and hence, for the given case, there is a smaller chance of it being any other class label. F2 and F3 are deemed the most important features whereas on the other hand all the other features have moderate to minimal amounts of... | [
"0.40",
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] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 89 | 37 | {'C1': '90.48%', 'C3': '9.51%', 'C2': '0.01%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F2 (equal to V4) and F3 (equa... | [
"F2",
"F3",
"F7",
"F9",
"F10",
"F11",
"F1",
"F6",
"F5",
"F4",
"F12",
"F8"
] | {'F2': 'Total_Stops', 'F3': 'Airline', 'F7': 'Destination', 'F9': 'Arrival_hour', 'F10': 'Source', 'F11': 'Duration_hours', 'F1': 'Dep_hour', 'F6': 'Dep_minute', 'F5': 'Arrival_minute', 'F4': 'Journey_month', 'F12': 'Journey_day', 'F8': 'Duration_mins'} | {'F12': 'F2', 'F9': 'F3', 'F11': 'F7', 'F5': 'F9', 'F10': 'F10', 'F7': 'F11', 'F3': 'F1', 'F4': 'F6', 'F6': 'F5', 'F2': 'F4', 'F1': 'F12', 'F8': 'F8'} | {'C1': 'C1', 'C3': 'C3', 'C2': 'C2'} | Low | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
DecisionTreeClassifier | C1 | Airline Passenger Satisfaction | Based on the probability distribution across the classes, the classifier is shown to have a moderately high confidence level in the C1 label assignment, with its likelihood equal to 65.0%, whereas that of C2 is only 35.0%. The prediction decision above is predominantly due to the influence of the variables F10, F15, F1... | [
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"neg... | 113 | 468 | {'C2': '35.00%', 'C1': '65.00%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F8 (equal to V2), F15 (equal to V1), F3 (with a value equal to V0) and F1... | [
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"F21",
"F11",
"F2",
"F25",
"F13",
"F24",
"F5",
"F23",
"F3",
"F16"
] | {'F10': 'X8', 'F15': 'X2', 'F17': 'X1', 'F4': 'X21', 'F9': 'X25', 'F14': 'X10', 'F1': 'X3', 'F26': 'X9', 'F7': 'X15', 'F18': 'X7', 'F12': 'X20', 'F22': 'X12', 'F20': 'X24', 'F8': 'X6', 'F6': 'X17', 'F19': 'X23', 'F21': 'X11', 'F11': 'X22', 'F2': 'X4', 'F25': 'X14', 'F13': 'X19', 'F24': 'X18', 'F5': 'X16', 'F23': 'X13',... | {'F8': 'F10', 'F2': 'F15', 'F1': 'F17', 'F21': 'F4', 'F25': 'F9', 'F10': 'F14', 'F3': 'F1', 'F9': 'F26', 'F15': 'F7', 'F7': 'F18', 'F20': 'F12', 'F12': 'F22', 'F24': 'F20', 'F6': 'F8', 'F17': 'F6', 'F23': 'F19', 'F11': 'F21', 'F22': 'F11', 'F4': 'F2', 'F14': 'F25', 'F19': 'F13', 'F18': 'F24', 'F16': 'F5', 'F13': 'F23',... | {'C1': 'C2', 'C2': 'C1'} | Acceptable | {'C2': 'neutral or dissatisfied', 'C1': 'satisfied'} |
KNeighborsClassifier | C1 | Company Bankruptcy Prediction | The model's output labelling judgement for the case under consideration is as follows: C2 cannot be the label for the given case; C1 is the most likely class label with a 100.0% confidence level. The key driving factors resulting in the aforementioned classification are the values of the input features: F63, F74, F14, ... | [
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"neg... | 423 | 352 | {'C1': '100.00%', 'C2': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
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"F6"... | {'F63': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F74': ' Net Income to Total Assets', 'F14': ' Realized Sales Gross Profit Growth Rate', 'F24': ' Accounts Receivable Turnover', 'F38': ' Operating Expense Rate', 'F42': ' Contingent liabilities\\/Net worth', 'F70': ' Non-industry income and expenditure\\/r... | {'F60': 'F63', 'F16': 'F74', 'F38': 'F14', 'F2': 'F24', 'F19': 'F38', 'F64': 'F42', 'F4': 'F70', 'F82': 'F73', 'F50': 'F31', 'F22': 'F45', 'F85': 'F81', 'F33': 'F71', 'F88': 'F4', 'F43': 'F10', 'F80': 'F77', 'F54': 'F16', 'F27': 'F33', 'F23': 'F27', 'F76': 'F41', 'F7': 'F21', 'F61': 'F62', 'F59': 'F9', 'F62': 'F49', 'F... | {'C1': 'C1', 'C2': 'C2'} | No | {'C1': 'No', 'C2': 'Yes'} |
BernoulliNB | C2 | Student Job Placement | For the case under consideration, the model assigned C2 with very high confidence, since the likelihood of C1 being the right label is only 0.52% which is very small. F11, F5, F3, and F6 have a large positive impact on the model's output prediction. F3 and F6 have a moderately positive impact on the prediction of C2, w... | [
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] | 21 | 8 | {'C1': '0.52%', 'C2': '99.48%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F3, F6, F9 and F10 (equal to V1)) on the model’s prediction of C2.",
"Sum... | [
"F11",
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"F3",
"F6",
"F9",
"F10",
"F12",
"F7",
"F8",
"F2",
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"F4"
] | {'F11': 'workex', 'F5': 'specialisation', 'F3': 'ssc_p', 'F6': 'hsc_p', 'F9': 'degree_p', 'F10': 'gender', 'F12': 'degree_t', 'F7': 'etest_p', 'F8': 'hsc_b', 'F2': 'hsc_s', 'F1': 'ssc_b', 'F4': 'mba_p'} | {'F11': 'F11', 'F12': 'F5', 'F1': 'F3', 'F2': 'F6', 'F3': 'F9', 'F6': 'F10', 'F10': 'F12', 'F4': 'F7', 'F8': 'F8', 'F9': 'F2', 'F7': 'F1', 'F5': 'F4'} | {'C2': 'C1', 'C1': 'C2'} | Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
LogisticRegression | C1 | Flight Price-Range Classification | Since the likelihood of C1 being the true label is shown by the prediction algorithm outputs to be equal to 93.02 percent, there is only a small chance that the true label for the given data instance is any of the other class labels, C3 and C2. The features F9, F1, F6, and F10 are the most important ones driving the la... | [
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"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
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] | 318 | 418 | {'C1': '93.02%', 'C3': '6.97%', 'C2': '0.01%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F9",
"F1",
"F10",
"F6",
"F11",
"F8",
"F2",
"F5",
"F3",
"F7",
"F12",
"F4"
] | {'F9': 'Total_Stops', 'F1': 'Airline', 'F10': 'Destination', 'F6': 'Journey_day', 'F11': 'Source', 'F8': 'Dep_hour', 'F2': 'Duration_hours', 'F5': 'Dep_minute', 'F3': 'Duration_mins', 'F7': 'Arrival_minute', 'F12': 'Arrival_hour', 'F4': 'Journey_month'} | {'F12': 'F9', 'F9': 'F1', 'F11': 'F10', 'F1': 'F6', 'F10': 'F11', 'F3': 'F8', 'F7': 'F2', 'F4': 'F5', 'F8': 'F3', 'F6': 'F7', 'F5': 'F12', 'F2': 'F4'} | {'C3': 'C1', 'C2': 'C3', 'C1': 'C2'} | Low | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
RandomForestClassifier | C2 | Cab Surge Pricing System | Between the three possible classes, there is an 88.0% probability that the correct label for this case is C2. This means that there is a 12.0% chance that the label could be one of the other possible labels, C3 or C1. Increasing the odds of the predicted label are the variables F3, F9, F12, and F1. The next set of vari... | [
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"positive",
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"positive",
"negative",
"negative",
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] | 171 | 438 | {'C3': '3.00%', 'C1': '9.00%', 'C2': '88.00%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the predic... | [
"F3",
"F9",
"F12",
"F1",
"F8",
"F6",
"F10",
"F4",
"F5",
"F2",
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"F7"
] | {'F3': 'Type_of_Cab', 'F9': 'Destination_Type', 'F12': 'Cancellation_Last_1Month', 'F1': 'Trip_Distance', 'F8': 'Customer_Rating', 'F6': 'Life_Style_Index', 'F10': 'Var3', 'F4': 'Var1', 'F5': 'Customer_Since_Months', 'F2': 'Var2', 'F11': 'Gender', 'F7': 'Confidence_Life_Style_Index'} | {'F2': 'F3', 'F6': 'F9', 'F8': 'F12', 'F1': 'F1', 'F7': 'F8', 'F4': 'F6', 'F11': 'F10', 'F9': 'F4', 'F3': 'F5', 'F10': 'F2', 'F12': 'F11', 'F5': 'F7'} | {'C2': 'C3', 'C3': 'C1', 'C1': 'C2'} | C3 | {'C3': 'Low', 'C1': 'Medium', 'C2': 'High'} |
RandomForestClassifier | C1 | Wine Quality Prediction | Based on the input variables, the model is moderately confident that the C1 is the appropriate label for the data under consideration. As a matter of fact, the prediction likelihood associated with class C2 is about 30.42%. The preceeding classification verdict can be largely blamed on the contributions of variables F2... | [
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"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F2",
"F8",
"F11",
"F1",
"F5",
"F7",
"F3",
"F10",
"F4",
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] | {'F2': 'alcohol', 'F8': 'sulphates', 'F11': 'volatile acidity', 'F1': 'total sulfur dioxide', 'F5': 'fixed acidity', 'F7': 'citric acid', 'F3': 'residual sugar', 'F10': 'density', 'F4': 'chlorides', 'F9': 'pH', 'F6': 'free sulfur dioxide'} | {'F11': 'F2', 'F10': 'F8', 'F2': 'F11', 'F7': 'F1', 'F1': 'F5', 'F3': 'F7', 'F4': 'F3', 'F8': 'F10', 'F5': 'F4', 'F9': 'F9', 'F6': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | high quality | {'C2': 'low_quality', 'C1': 'high quality'} |
SVC | C2 | E-Commerce Shipping | The classifier is 69.02% certain that the given case is under the class label C2, implying that the likelihood of C1 is only 30.98%. Analysis performed to understand the contribution of each input feature revealed that: F6, F4, and F2 are the most influential features when assigning a label to the given case. Features ... | [
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"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F6 and F4) on the prediction made for this test case.",
"Compare the direction of impact of the features: F2 (value equal to V3), F5 (when it is equal to V... | [
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"F2",
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"F7",
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] | {'F6': 'Weight_in_gms', 'F4': 'Discount_offered', 'F2': 'Prior_purchases', 'F5': 'Customer_care_calls', 'F3': 'Cost_of_the_Product', 'F7': 'Mode_of_Shipment', 'F8': 'Customer_rating', 'F10': 'Gender', 'F1': 'Product_importance', 'F9': 'Warehouse_block'} | {'F3': 'F6', 'F2': 'F4', 'F8': 'F2', 'F6': 'F5', 'F1': 'F3', 'F5': 'F7', 'F7': 'F8', 'F10': 'F10', 'F9': 'F1', 'F4': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | On-time | {'C2': 'On-time', 'C1': 'Late'} |
RandomForestClassifier | C2 | Advertisement Prediction | The classifier trained on this prediction problem assigns a label to a given case based on the information supplied. The class assigned by the classifier to the case under consideration is C2. The probability that C1 is the correct label is around 25.28%; therefore, it is less likely to be the true label. The above cla... | [
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] | [
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive"
] | 31 | 385 | {'C2': '74.72%', 'C1': '25.28%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F3 and F6.",
"Compare and contrast the impact of the following features (F1, F4 (when it is equal to V1), F2 and F7 (when it is equal to V1)... | [
"F3",
"F6",
"F1",
"F4",
"F2",
"F7",
"F5"
] | {'F3': 'Daily Time Spent on Site', 'F6': 'Daily Internet Usage', 'F1': 'Age', 'F4': 'ad_day', 'F2': 'Area Income', 'F7': 'Gender', 'F5': 'ad_month'} | {'F1': 'F3', 'F4': 'F6', 'F2': 'F1', 'F7': 'F4', 'F3': 'F2', 'F5': 'F7', 'F6': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Skip | {'C2': 'Skip', 'C1': 'Watch'} |
GradientBoostingClassifier | C1 | Food Ordering Customer Churn Prediction | The case given is labelled as C1 by the classifier with a confidence level equal to 82.07%. Therefore, the probability of C2 being the correct label is only 17.93%. The classification above is mainly due to the contributions of features such as F29, F3, F40, and F8. F45, F25, and F34 are the next three with moderate in... | [
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"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F29",
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... | {'F29': 'More restaurant choices', 'F3': 'Ease and convenient', 'F40': 'Bad past experience', 'F8': 'Time saving', 'F45': 'Easy Payment option', 'F25': 'Good Tracking system', 'F34': 'Wrong order delivered', 'F39': 'Influence of rating', 'F41': 'Late Delivery', 'F9': 'Less Delivery time', 'F11': 'Long delivery time', '... | {'F12': 'F29', 'F10': 'F3', 'F21': 'F40', 'F11': 'F8', 'F13': 'F45', 'F16': 'F25', 'F27': 'F34', 'F38': 'F39', 'F19': 'F41', 'F39': 'F9', 'F24': 'F11', 'F37': 'F15', 'F29': 'F19', 'F14': 'F26', 'F43': 'F35', 'F22': 'F23', 'F26': 'F20', 'F20': 'F24', 'F31': 'F21', 'F25': 'F43', 'F40': 'F2', 'F33': 'F44', 'F45': 'F38', '... | {'C2': 'C2', 'C1': 'C1'} | Go Away | {'C2': 'Return', 'C1': 'Go Away'} |
SVM_linear | C4 | Mobile Price-Range Classification | According to the algorithm, there is little to no chance that the correct label for the given data instance is any of the following classes: C3, C2, and C1. It is very confident that the proper label is C4. This label assignment is largely due to the parts played by the features F8, F1, and F9. On the lower end are th... | [
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"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative"
] | 227 | 134 | {'C3': '0.00%', 'C2': '0.00%', 'C1': '0.00%', 'C4': '100.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F8",
"F1",
"F9",
"F20",
"F6",
"F11",
"F10",
"F19",
"F18",
"F3",
"F12",
"F4",
"F17",
"F2",
"F13",
"F5",
"F16",
"F14",
"F15",
"F7"
] | {'F8': 'ram', 'F1': 'battery_power', 'F9': 'px_width', 'F20': 'int_memory', 'F6': 'sc_h', 'F11': 'pc', 'F10': 'mobile_wt', 'F19': 'fc', 'F18': 'n_cores', 'F3': 'clock_speed', 'F12': 'blue', 'F4': 'three_g', 'F17': 'touch_screen', 'F2': 'm_dep', 'F13': 'px_height', 'F5': 'talk_time', 'F16': 'dual_sim', 'F14': 'wifi', 'F... | {'F11': 'F8', 'F1': 'F1', 'F10': 'F9', 'F4': 'F20', 'F12': 'F6', 'F8': 'F11', 'F6': 'F10', 'F3': 'F19', 'F7': 'F18', 'F2': 'F3', 'F15': 'F12', 'F18': 'F4', 'F19': 'F17', 'F5': 'F2', 'F9': 'F13', 'F14': 'F5', 'F16': 'F16', 'F20': 'F14', 'F17': 'F15', 'F13': 'F7'} | {'C4': 'C3', 'C1': 'C2', 'C3': 'C1', 'C2': 'C4'} | r4 | {'C3': 'r1', 'C2': 'r2', 'C1': 'r3', 'C4': 'r4'} |
SVC | C2 | Paris House Classification | The model predicts that the label for this case is C2 with a high degree of certainty of about 99.19% and the probability of the other label is only 0.81%. From the analysis, the variables with the strongest attributions to this classification decision are F14, F12, and F4. The attributions of these variables increased... | [
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"positive",
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"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 168 | 94 | {'C2': '99.19%', 'C1': '0.81%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F12, F16, F17 and F8) on the model’s prediction of C2.",
"Summarize the se... | [
"F14",
"F4",
"F12",
"F16",
"F17",
"F8",
"F7",
"F3",
"F13",
"F11",
"F10",
"F15",
"F6",
"F1",
"F2",
"F9",
"F5"
] | {'F14': 'isNewBuilt', 'F4': 'hasYard', 'F12': 'hasPool', 'F16': 'hasStormProtector', 'F17': 'hasStorageRoom', 'F8': 'made', 'F7': 'basement', 'F3': 'numberOfRooms', 'F13': 'squareMeters', 'F11': 'floors', 'F10': 'numPrevOwners', 'F15': 'garage', 'F6': 'attic', 'F1': 'cityCode', 'F2': 'price', 'F9': 'cityPartRange', 'F5... | {'F3': 'F14', 'F1': 'F4', 'F2': 'F12', 'F4': 'F16', 'F5': 'F17', 'F12': 'F8', 'F13': 'F7', 'F7': 'F3', 'F6': 'F13', 'F8': 'F11', 'F11': 'F10', 'F15': 'F15', 'F14': 'F6', 'F9': 'F1', 'F17': 'F2', 'F10': 'F9', 'F16': 'F5'} | {'C2': 'C2', 'C1': 'C1'} | Basic | {'C2': 'Basic', 'C1': 'Luxury'} |
LogisticRegression | C1 | Used Cars Price-Range Prediction | According to the output prediction probabilities across the two classes, the output decision for the given data is C1 with a very high confidence level. C2 has a prediction probability of about 0.00%. The variables contributing most to the abovementioned classification are F2, F10, and F1, whereas F3 and F4 are the lea... | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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] | {'F2': 'car_age', 'F10': 'Power', 'F1': 'Fuel_Type', 'F8': 'Engine', 'F5': 'Seats', 'F6': 'Transmission', 'F7': 'Kilometers_Driven', 'F9': 'Name', 'F3': 'Mileage', 'F4': 'Owner_Type'} | {'F5': 'F2', 'F4': 'F10', 'F7': 'F1', 'F3': 'F8', 'F10': 'F5', 'F8': 'F6', 'F1': 'F7', 'F6': 'F9', 'F2': 'F3', 'F9': 'F4'} | {'C2': 'C1', 'C1': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
SVC | C1 | Tic-Tac-Toe Strategy | With a labelling confidence level of 99.50%, the classifier predicts the label C1 in this situation. Hence, it is correct to conclude that the classifier is less certain that C2 is the proper label for the case here. The analysis indicates that five features contradict the decision above, while four features support th... | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F4",
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"F5",
"F1",
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"F8",
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] | {'F4': 'middle-middle-square', 'F3': 'top-left-square', 'F5': 'bottom-left-square', 'F1': 'bottom-right-square', 'F9': ' top-right-square', 'F8': 'middle-right-square', 'F7': 'top-middle-square', 'F2': 'middle-left-square', 'F6': 'bottom-middle-square'} | {'F5': 'F4', 'F1': 'F3', 'F7': 'F5', 'F9': 'F1', 'F3': 'F9', 'F6': 'F8', 'F2': 'F7', 'F4': 'F2', 'F8': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | player B win | {'C2': 'player B lose', 'C1': 'player B win'} |
SVMClassifier_poly | C2 | Employee Attrition | The class assigned by the model is C2 with a close to 97.67% confidence level, implying that the likelihood of C1 is only 2.33%. Based on the analysis, the most important features considered during the classification are F6, F1, F29, and F9 but among these features, F1 and F29 are the only ones with negative attributio... | [
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"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the predic... | [
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"F24",
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] | {'F6': 'OverTime', 'F1': 'JobSatisfaction', 'F29': 'BusinessTravel', 'F9': 'MaritalStatus', 'F11': 'EnvironmentSatisfaction', 'F10': 'Department', 'F23': 'Age', 'F27': 'YearsInCurrentRole', 'F26': 'TotalWorkingYears', 'F3': 'WorkLifeBalance', 'F17': 'JobLevel', 'F7': 'JobInvolvement', 'F25': 'EducationField', 'F2': 'Jo... | {'F26': 'F6', 'F30': 'F1', 'F17': 'F29', 'F25': 'F9', 'F28': 'F11', 'F21': 'F10', 'F1': 'F23', 'F14': 'F27', 'F11': 'F26', 'F20': 'F3', 'F5': 'F17', 'F29': 'F7', 'F22': 'F25', 'F24': 'F2', 'F6': 'F21', 'F19': 'F13', 'F3': 'F4', 'F27': 'F14', 'F23': 'F8', 'F16': 'F19', 'F9': 'F20', 'F18': 'F12', 'F7': 'F15', 'F2': 'F28'... | {'C2': 'C2', 'C1': 'C1'} | Stay | {'C2': 'Leave', 'C1': 'Leave'} |
KNeighborsClassifier | C1 | Advertisement Prediction | With a higher degree of confidence, the model labels this given case as C1 since there is a zero chance that it is C2. The classification here can be attributed to all the features having positive contributions, decreasing the odds of C2 being the correct label. The features can be ranked based on their degree of influ... | [
"0.47",
"0.22",
"0.20",
"0.19",
"0.05",
"0.01",
"0.01"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive"
] | 253 | 163 | {'C2': '0.00%', 'C1': '100.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F5",
"F6",
"F3",
"F1",
"F7",
"F4",
"F2"
] | {'F5': 'Daily Time Spent on Site', 'F6': 'Area Income', 'F3': 'Age', 'F1': 'Daily Internet Usage', 'F7': 'ad_day', 'F4': 'Gender', 'F2': 'ad_month'} | {'F1': 'F5', 'F3': 'F6', 'F2': 'F3', 'F4': 'F1', 'F7': 'F7', 'F5': 'F4', 'F6': 'F2'} | {'C2': 'C2', 'C1': 'C1'} | Watch | {'C2': 'Skip', 'C1': 'Watch'} |
SVM_poly | C2 | Mobile Price-Range Classification | According to the model, C2 has a prediction probability of 99.45 percent, C3 has a prediction probability of 0.47 percent, C1 has a prediction probability of 0.04 percent, and C4 has a prediction probability of 0.05 percent, therefore, the most likely class is C2. F13 and F19 positively influence the above-mentioned la... | [
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] | 47 | 263 | {'C2': '99.45%', 'C3': '0.47%', 'C1': '0.04%', 'C4': '0.05%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F19, F13 and F9.",
"Compare and contrast the impact of the following features (F16, F20 (value equal to V1) and F8 (value equal to V1)) on t... | [
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"F15",
"F18",
"F4",
"F6",
"F3",
"F14",
"F10",
"F1",
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] | {'F19': 'ram', 'F13': 'battery_power', 'F9': 'px_height', 'F16': 'px_width', 'F20': 'dual_sim', 'F8': 'four_g', 'F11': 'touch_screen', 'F7': 'int_memory', 'F17': 'pc', 'F5': 'n_cores', 'F2': 'fc', 'F15': 'clock_speed', 'F18': 'three_g', 'F4': 'sc_w', 'F6': 'wifi', 'F3': 'm_dep', 'F14': 'mobile_wt', 'F10': 'talk_time', ... | {'F11': 'F19', 'F1': 'F13', 'F9': 'F9', 'F10': 'F16', 'F16': 'F20', 'F17': 'F8', 'F19': 'F11', 'F4': 'F7', 'F8': 'F17', 'F7': 'F5', 'F3': 'F2', 'F2': 'F15', 'F18': 'F18', 'F13': 'F4', 'F20': 'F6', 'F5': 'F3', 'F6': 'F14', 'F14': 'F10', 'F12': 'F1', 'F15': 'F12'} | {'C1': 'C2', 'C2': 'C3', 'C4': 'C1', 'C3': 'C4'} | r1 | {'C2': 'r1', 'C3': 'r2', 'C1': 'r3', 'C4': 'r4'} |
GradientBoostingClassifier | C1 | Food Ordering Customer Churn Prediction | Per the model employed here, the prediction probability of C2 is only 17.93%, and that of C1 is equal to 82.07%. Given the information provided to the model, the most valid conclusion regarding the true label is that C1 is without a doubt the most likely one. The attributions analysis indicates that F36, F32, F3, F30, ... | [
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"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F14, F15 and F6) on the model’s prediction of C1.",
"Summarize the set of ... | [
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... | {'F36': 'Ease and convenient', 'F32': 'More restaurant choices', 'F3': 'Bad past experience', 'F30': 'More Offers and Discount', 'F14': 'Unavailability', 'F15': 'Good Food quality', 'F6': 'Low quantity low time', 'F1': 'Delay of delivery person getting assigned', 'F17': 'Late Delivery', 'F2': 'Less Delivery time', 'F34... | {'F10': 'F36', 'F12': 'F32', 'F21': 'F3', 'F14': 'F30', 'F22': 'F14', 'F15': 'F15', 'F36': 'F6', 'F25': 'F1', 'F19': 'F17', 'F39': 'F2', 'F33': 'F34', 'F43': 'F4', 'F6': 'F25', 'F38': 'F27', 'F4': 'F38', 'F8': 'F10', 'F37': 'F12', 'F45': 'F35', 'F24': 'F46', 'F17': 'F5', 'F30': 'F28', 'F40': 'F8', 'F41': 'F41', 'F35': ... | {'C1': 'C1', 'C2': 'C2'} | Return | {'C1': 'Return', 'C2': 'Go Away'} |
RandomForestClassifier | C2 | Personal Loan Modelling | The model is about 90.0% certain or sure that the correct label based on the input features of the given case is C2. The features with the most significant influence on the decision are F9, F3, F1, and F7. The influence of the features can be categorised as positive or negative traits depending on the direction of the ... | [
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] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 215 | 447 | {'C1': '10.00%', 'C2': '90.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F3",
"F9",
"F1",
"F7",
"F2",
"F4",
"F6",
"F5",
"F8"
] | {'F3': 'Income', 'F9': 'CCAvg', 'F1': 'CD Account', 'F7': 'Education', 'F2': 'Extra_service', 'F4': 'Securities Account', 'F6': 'Family', 'F5': 'Mortgage', 'F8': 'Age'} | {'F2': 'F3', 'F4': 'F9', 'F8': 'F1', 'F5': 'F7', 'F9': 'F2', 'F7': 'F4', 'F3': 'F6', 'F6': 'F5', 'F1': 'F8'} | {'C2': 'C1', 'C1': 'C2'} | Accept | {'C1': 'Reject', 'C2': 'Accept'} |
LogisticRegression | C2 | Tic-Tac-Toe Strategy | With an 81.01% chance of being correct, C2 is the most likely label, consequently, the C1 class's prediction probability is only 18.99%. The algorithm or classifier got the above prediction mostly due to the influence of features like F2, F8, F9, and F4. F3, which is found to have very little impact with regard to the ... | [
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] | [
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative"
] | 231 | 307 | {'C1': '18.99%', 'C2': '81.01%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F8",
"F2",
"F9",
"F4",
"F6",
"F5",
"F1",
"F7",
"F3"
] | {'F8': 'bottom-right-square', 'F2': 'middle-middle-square', 'F9': 'bottom-left-square', 'F4': 'middle-left-square', 'F6': 'top-left-square', 'F5': ' top-right-square', 'F1': 'middle-right-square', 'F7': 'top-middle-square', 'F3': 'bottom-middle-square'} | {'F9': 'F8', 'F5': 'F2', 'F7': 'F9', 'F4': 'F4', 'F1': 'F6', 'F3': 'F5', 'F6': 'F1', 'F2': 'F7', 'F8': 'F3'} | {'C2': 'C1', 'C1': 'C2'} | player B win | {'C1': 'player B lose', 'C2': 'player B win'} |
SVC | C2 | Student Job Placement | The model makes classification decisions based on the information provided to it and for the case here, the prediction probabilities across the two class labels, C1 and C2, are 49.32% and 50.68%, respectively. Based on these prediction probabilities, the label assigned is C2, since it has the highest likelihood, howeve... | [
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"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
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] | 440 | 204 | {'C1': '49.32%', 'C2': '50.68%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F4",
"F8",
"F9",
"F7",
"F5",
"F3",
"F12",
"F2",
"F11",
"F6",
"F1",
"F10"
] | {'F4': 'mba_p', 'F8': 'specialisation', 'F9': 'etest_p', 'F7': 'gender', 'F5': 'workex', 'F3': 'hsc_s', 'F12': 'hsc_p', 'F2': 'degree_t', 'F11': 'ssc_p', 'F6': 'degree_p', 'F1': 'ssc_b', 'F10': 'hsc_b'} | {'F5': 'F4', 'F12': 'F8', 'F4': 'F9', 'F6': 'F7', 'F11': 'F5', 'F9': 'F3', 'F2': 'F12', 'F10': 'F2', 'F1': 'F11', 'F3': 'F6', 'F7': 'F1', 'F8': 'F10'} | {'C2': 'C1', 'C1': 'C2'} | Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
SVMClassifier_liner | C1 | Employee Attrition | The most likely label for the given case is C1 since the predicted probability of C2 is only 34.27% and this means that the likelihood of C1 is 65.73%. The most relevant features that led to the C1 classification verdict are F11, F10, F9, F27, and F2. However, some of the features are deemed irrelevant to the above ver... | [
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"neg... | 206 | 121 | {'C1': '65.73%', 'C2': '34.27%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F11 and F10.",
"Summarize th... | [
"F11",
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"F9",
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"F20",
"F21",
"F3",
"F6",
"F22",
"F29",
"F5",
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"F26"
] | {'F11': 'OverTime', 'F10': 'NumCompaniesWorked', 'F9': 'YearsSinceLastPromotion', 'F27': 'BusinessTravel', 'F2': 'MaritalStatus', 'F12': 'RelationshipSatisfaction', 'F16': 'Department', 'F23': 'Age', 'F30': 'Gender', 'F28': 'JobInvolvement', 'F15': 'JobRole', 'F1': 'PerformanceRating', 'F19': 'EnvironmentSatisfaction',... | {'F26': 'F11', 'F8': 'F10', 'F15': 'F9', 'F17': 'F27', 'F25': 'F2', 'F18': 'F12', 'F21': 'F16', 'F1': 'F23', 'F23': 'F30', 'F29': 'F28', 'F24': 'F15', 'F19': 'F1', 'F28': 'F19', 'F2': 'F7', 'F13': 'F18', 'F16': 'F24', 'F27': 'F25', 'F22': 'F8', 'F20': 'F13', 'F3': 'F17', 'F14': 'F14', 'F12': 'F20', 'F11': 'F21', 'F10':... | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Leave', 'C2': 'Leave'} |
RandomForestClassifier | C2 | Printer Sales | Per the classifier for the given data, the most plausible label is C2. F4, F16, F11, and F25 are the main features pushing for the above-mentioned outcome. F3, F20, F8, F24, F15, and F6, on the other hand, have little contribution to the classifier employed here. F12, F10, F26, and F14 have a moderate contribution to t... | [
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"neg... | 242 | 319 | {'C1': '20.00%', 'C2': '80.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
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] | {'F16': 'X24', 'F11': 'X1', 'F4': 'X8', 'F25': 'X21', 'F26': 'X4', 'F10': 'X10', 'F12': 'X3', 'F14': 'X15', 'F13': 'X9', 'F5': 'X23', 'F7': 'X25', 'F22': 'X7', 'F2': 'X22', 'F21': 'X11', 'F18': 'X17', 'F17': 'X18', 'F19': 'X26', 'F9': 'X13', 'F23': 'X6', 'F1': 'X20', 'F24': 'X16', 'F6': 'X19', 'F8': 'X2', 'F3': 'X12', ... | {'F24': 'F16', 'F1': 'F11', 'F8': 'F4', 'F21': 'F25', 'F4': 'F26', 'F10': 'F10', 'F3': 'F12', 'F15': 'F14', 'F9': 'F13', 'F23': 'F5', 'F25': 'F7', 'F7': 'F22', 'F22': 'F2', 'F11': 'F21', 'F17': 'F18', 'F18': 'F17', 'F26': 'F19', 'F13': 'F9', 'F6': 'F23', 'F20': 'F1', 'F16': 'F24', 'F19': 'F6', 'F2': 'F8', 'F12': 'F3', ... | {'C1': 'C1', 'C2': 'C2'} | More | {'C1': 'Less', 'C2': 'More'} |
SGDClassifier | C2 | Job Change of Data Scientists | The least probable class, according to the classification algorithm, is C1, with a prediction probability of 25.12%, therefore, we can conclude that the algorithm is quite confident that the correct label for this data is C2. Analysing the attributions revealed that F6, F1, F10, and F11 are the most relevant features, ... | [
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] | 223 | 449 | {'C2': '74.88%', 'C1': '25.12%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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] | {'F6': 'city_development_index', 'F10': 'relevent_experience', 'F1': 'city', 'F11': 'major_discipline', 'F12': 'experience', 'F7': 'training_hours', 'F9': 'education_level', 'F8': 'gender', 'F5': 'enrolled_university', 'F3': 'company_type', 'F4': 'last_new_job', 'F2': 'company_size'} | {'F1': 'F6', 'F5': 'F10', 'F3': 'F1', 'F8': 'F11', 'F9': 'F12', 'F2': 'F7', 'F7': 'F9', 'F4': 'F8', 'F6': 'F5', 'F11': 'F3', 'F12': 'F4', 'F10': 'F2'} | {'C1': 'C2', 'C2': 'C1'} | Stay | {'C2': 'Stay', 'C1': 'Leave'} |
SVM_poly | C1 | Mobile Price-Range Classification | According to the classification algorithm, neither C2 nor C3 nor C4 is the correct label for the given case. It is 100.0% certain that C1 is the right label. The higher degree of certainty in the above prediction can be attributed to the positive contributions of F20, F11, and F6. The other positive features include F4... | [
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"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
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] | {'F20': 'ram', 'F11': 'battery_power', 'F6': 'px_width', 'F7': 'int_memory', 'F10': 'sc_h', 'F3': 'wifi', 'F4': 'fc', 'F9': 'three_g', 'F12': 'mobile_wt', 'F18': 'clock_speed', 'F19': 'm_dep', 'F5': 'n_cores', 'F16': 'pc', 'F14': 'touch_screen', 'F1': 'blue', 'F17': 'talk_time', 'F8': 'sc_w', 'F2': 'px_height', 'F15': ... | {'F11': 'F20', 'F1': 'F11', 'F10': 'F6', 'F4': 'F7', 'F12': 'F10', 'F20': 'F3', 'F3': 'F4', 'F18': 'F9', 'F6': 'F12', 'F2': 'F18', 'F5': 'F19', 'F7': 'F5', 'F8': 'F16', 'F19': 'F14', 'F15': 'F1', 'F14': 'F17', 'F13': 'F8', 'F9': 'F2', 'F17': 'F15', 'F16': 'F13'} | {'C1': 'C2', 'C3': 'C3', 'C2': 'C4', 'C4': 'C1'} | r4 | {'C2': 'r1', 'C3': 'r2', 'C4': 'r3', 'C1': 'r4'} |
DNN | C2 | Ethereum Fraud Detection | The prediction likelihoods across the two classes are 15.35% for class C1 and 84.65% for C2, it can be concluded that C2 is the most probable class label for the given data instance. According to the attribution analysis conducted, the different input variables have varying degrees of influence on the model's decision ... | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F9, F24, F18, F15 and F21.",
... | [
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"... | {'F9': ' ERC20 uniq rec contract addr', 'F24': ' ERC20 uniq rec token name', 'F18': 'min value received', 'F15': 'Time Diff between first and last (Mins)', 'F21': 'avg val sent', 'F28': ' ERC20 uniq sent token name', 'F13': 'Sent tnx', 'F36': 'Avg min between received tnx', 'F10': 'Unique Received From Addresses', 'F5'... | {'F30': 'F9', 'F38': 'F24', 'F9': 'F18', 'F3': 'F15', 'F14': 'F21', 'F37': 'F28', 'F4': 'F13', 'F2': 'F36', 'F7': 'F10', 'F28': 'F5', 'F18': 'F14', 'F1': 'F31', 'F29': 'F6', 'F11': 'F8', 'F8': 'F1', 'F10': 'F12', 'F13': 'F35', 'F12': 'F2', 'F6': 'F30', 'F20': 'F25', 'F27': 'F11', 'F24': 'F32', 'F5': 'F23', 'F36': 'F17'... | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
KNeighborsClassifier | C1 | Credit Risk Classification | According to the machine learning model, it is more likely that the case's label is C1, with a certainty of 100.0%, and this prediction decision is mainly based on the effects of the following features: F5, F3, F11, F7, and F1 on the model. Apart from F1 and F7, all the other variables mentioned above have a strong pos... | [
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"positive",
"negative",
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"negative",
"negative",
"positive",
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] | 115 | 290 | {'C1': '100.00%', 'C2': '0.00%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F5, F3, F11 and F1) on the prediction made for this test case.",
"Compare the direction of impact of the features: F7, F10 and F8.",
"Describe the degree o... | [
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"F3",
"F11",
"F1",
"F7",
"F10",
"F8",
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] | {'F5': 'fea_4', 'F3': 'fea_8', 'F11': 'fea_2', 'F1': 'fea_9', 'F7': 'fea_6', 'F10': 'fea_10', 'F8': 'fea_1', 'F9': 'fea_7', 'F4': 'fea_11', 'F6': 'fea_3', 'F2': 'fea_5'} | {'F4': 'F5', 'F8': 'F3', 'F2': 'F11', 'F9': 'F1', 'F6': 'F7', 'F10': 'F10', 'F1': 'F8', 'F7': 'F9', 'F11': 'F4', 'F3': 'F6', 'F5': 'F2'} | {'C2': 'C1', 'C1': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
SVMClassifier_poly | C1 | Employee Attrition | The classification findings by the model for the case here are as follows: there is a 97.67% chance that C1 is the correct label hence only a marginally low chance of 2.33% that C1 is not the correct label but C2 is. From the above findings, it is valid to conclude that the right class for the given case is C1, and the... | [
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"neg... | 254 | 164 | {'C1': '97.67%', 'C2': '2.33%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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"F30",
"F18",
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] | {'F22': 'OverTime', 'F26': 'JobSatisfaction', 'F17': 'BusinessTravel', 'F11': 'MaritalStatus', 'F29': 'EnvironmentSatisfaction', 'F13': 'Department', 'F3': 'Age', 'F6': 'YearsInCurrentRole', 'F7': 'TotalWorkingYears', 'F8': 'WorkLifeBalance', 'F12': 'JobLevel', 'F10': 'JobInvolvement', 'F5': 'EducationField', 'F2': 'Jo... | {'F26': 'F22', 'F30': 'F26', 'F17': 'F17', 'F25': 'F11', 'F28': 'F29', 'F21': 'F13', 'F1': 'F3', 'F14': 'F6', 'F11': 'F7', 'F20': 'F8', 'F5': 'F12', 'F29': 'F10', 'F22': 'F5', 'F24': 'F2', 'F6': 'F23', 'F19': 'F20', 'F3': 'F16', 'F27': 'F14', 'F23': 'F9', 'F16': 'F28', 'F9': 'F15', 'F18': 'F24', 'F7': 'F1', 'F2': 'F4',... | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Leave', 'C2': 'Leave'} |
LogisticRegression | C1 | Flight Price-Range Classification | The model is very confident that C1 is the most probable class for the given case, with a probability of 90.48% which means that the other labels are very unlikely. F5 and F9 are the most important variables with respect to this classification verdict while all other variables are shown to have a medium or low impact. ... | [
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"positive",
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"positive",
"positive",
"negative",
"positive",
"positive",
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"positive",
"positive",
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] | 89 | 244 | {'C1': '90.48%', 'C3': '9.51%', 'C2': '0.01%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F5 (equal to V4) and F9 (equa... | [
"F5",
"F9",
"F2",
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"F8",
"F4",
"F3",
"F11",
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"F12"
] | {'F5': 'Total_Stops', 'F9': 'Airline', 'F2': 'Destination', 'F7': 'Arrival_hour', 'F8': 'Source', 'F4': 'Duration_hours', 'F3': 'Dep_hour', 'F11': 'Dep_minute', 'F10': 'Arrival_minute', 'F6': 'Journey_month', 'F1': 'Journey_day', 'F12': 'Duration_mins'} | {'F12': 'F5', 'F9': 'F9', 'F11': 'F2', 'F5': 'F7', 'F10': 'F8', 'F7': 'F4', 'F3': 'F3', 'F4': 'F11', 'F6': 'F10', 'F2': 'F6', 'F1': 'F1', 'F8': 'F12'} | {'C3': 'C1', 'C1': 'C3', 'C2': 'C2'} | Low | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
SVC | C2 | Water Quality Classification | Despite the reasonably high confidence in the assigned label, the prediction probabilities across the two classes indicate that C1 might be the correct label. F4, F7, F2, and F8 are the factors whose major contributions resulted in the labelling choice mentioned above. According to the analysis, the top two factors, F4... | [
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] | [
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
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"positive",
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] | 237 | 326 | {'C1': '38.68%', 'C2': '61.32%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F4",
"F7",
"F2",
"F8",
"F1",
"F9",
"F5",
"F3",
"F6"
] | {'F4': 'Sulfate', 'F7': 'Hardness', 'F2': 'ph', 'F8': 'Conductivity', 'F1': 'Turbidity', 'F9': 'Chloramines', 'F5': 'Solids', 'F3': 'Trihalomethanes', 'F6': 'Organic_carbon'} | {'F5': 'F4', 'F2': 'F7', 'F1': 'F2', 'F6': 'F8', 'F9': 'F1', 'F4': 'F9', 'F3': 'F5', 'F8': 'F3', 'F7': 'F6'} | {'C2': 'C1', 'C1': 'C2'} | Portable | {'C1': 'Not Portable', 'C2': 'Portable'} |
MLPClassifier | C2 | Ethereum Fraud Detection | C1 has a probability estimate of only 6.80%, while that of C2 is 93.20%; consequently, the most likely class for the given case is C2. The important or relevant features considered by the classifier are F11, F16, F4, F15, F28, F14, F9, F32, F17, F21, F23, F33, F26, F18, F2, F5, F37, F3, F7, and F1. Not all input featur... | [
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"neg... | 243 | 317 | {'C1': '6.80%', 'C2': '93.20%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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... | {'F11': 'Unique Received From Addresses', 'F16': ' ERC20 total Ether sent contract', 'F4': 'total ether received', 'F15': 'Sent tnx', 'F28': 'Number of Created Contracts', 'F14': ' ERC20 uniq rec token name', 'F9': ' ERC20 uniq rec contract addr', 'F32': 'max value received ', 'F17': 'total transactions (including tnx ... | {'F7': 'F11', 'F26': 'F16', 'F20': 'F4', 'F4': 'F15', 'F6': 'F28', 'F38': 'F14', 'F30': 'F9', 'F10': 'F32', 'F18': 'F17', 'F29': 'F21', 'F27': 'F23', 'F5': 'F33', 'F11': 'F26', 'F28': 'F18', 'F14': 'F2', 'F9': 'F5', 'F8': 'F37', 'F37': 'F3', 'F2': 'F7', 'F3': 'F1', 'F31': 'F38', 'F32': 'F30', 'F34': 'F13', 'F35': 'F22'... | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
BernoulliNB | C1 | German Credit Evaluation | The model is not 100% convinced that the correct label for the data under consideration is C1 since there is a 26.27% chance that labelling the data as C2 is correct. All the input variables are shown to have some degree of influence on the classification decision, with the most influential variables being F2, F5, and... | [
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] | [
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative"
] | 295 | 185 | {'C1': '73.73%', 'C2': '26.27%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F2",
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] | {'F2': 'Saving accounts', 'F5': 'Sex', 'F3': 'Housing', 'F7': 'Purpose', 'F6': 'Checking account', 'F8': 'Job', 'F9': 'Duration', 'F4': 'Age', 'F1': 'Credit amount'} | {'F5': 'F2', 'F2': 'F5', 'F4': 'F3', 'F9': 'F7', 'F6': 'F6', 'F3': 'F8', 'F8': 'F9', 'F1': 'F4', 'F7': 'F1'} | {'C2': 'C1', 'C1': 'C2'} | Good Credit | {'C1': 'Good Credit', 'C2': 'Bad Credit'} |
SVMClassifier_poly | C2 | Employee Attrition | The model predicted class C2 with an 81.98% prediction likelihood. F26 had the largest impact, followed by F6, F25, F16, F21, F28, F8, F3, F12, F15, F2, F20, F24, F5, F22, F11, F4, F1, F9, and finally, F13, which had the smallest non-zero impact. F26, the feature with the largest impact, contributed against the directi... | [
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"neg... | 98 | 44 | {'C2': '81.98%', 'C1': '18.02%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F26 (with a value equal to V1... | [
"F26",
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"F27",
"F7",
"F18",
"F29",
"F14",
"F19",
"F10",
"F30",
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] | {'F26': 'OverTime', 'F6': 'JobSatisfaction', 'F25': 'MaritalStatus', 'F16': 'Department', 'F21': 'NumCompaniesWorked', 'F28': 'BusinessTravel', 'F8': 'JobRole', 'F3': 'EnvironmentSatisfaction', 'F12': 'YearsInCurrentRole', 'F15': 'JobInvolvement', 'F2': 'WorkLifeBalance', 'F20': 'YearsSinceLastPromotion', 'F24': 'Total... | {'F26': 'F26', 'F30': 'F6', 'F25': 'F25', 'F21': 'F16', 'F8': 'F21', 'F17': 'F28', 'F24': 'F8', 'F28': 'F3', 'F14': 'F12', 'F29': 'F15', 'F20': 'F2', 'F15': 'F20', 'F11': 'F24', 'F5': 'F5', 'F1': 'F22', 'F22': 'F11', 'F19': 'F4', 'F7': 'F1', 'F27': 'F9', 'F6': 'F13', 'F2': 'F27', 'F13': 'F7', 'F18': 'F18', 'F12': 'F29'... | {'C1': 'C2', 'C2': 'C1'} | Stay | {'C2': 'Leave', 'C1': 'Leave'} |
SVC | C1 | German Credit Evaluation | This case's label has a 70.83 percent chance of being C1 and per the predicted likelihoods across the alternative labels, C3 has a 29.71 percent chance of being the correct label, however, the model is certain that C2 is not the true label. The most important variables are F5, F4, F9, and F1, whereas the remaining infl... | [
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] | [
"positive",
"negative",
"negative",
"negative",
"positive",
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] | 136 | 300 | {'C1': '70.83%', 'C3': '29.17%', 'C2': '0.0%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F1, F5, F4, F9 and F7.",
"Compare and contrast the impact of the following features (F2, F3 and F6) on the model’s prediction of C1.",
"Desc... | [
"F1",
"F5",
"F4",
"F9",
"F7",
"F2",
"F3",
"F6",
"F8"
] | {'F1': 'Checking account', 'F5': 'Duration', 'F4': 'Housing', 'F9': 'Saving accounts', 'F7': 'Sex', 'F2': 'Age', 'F3': 'Purpose', 'F6': 'Job', 'F8': 'Credit amount'} | {'F6': 'F1', 'F8': 'F5', 'F4': 'F4', 'F5': 'F9', 'F2': 'F7', 'F1': 'F2', 'F9': 'F3', 'F3': 'F6', 'F7': 'F8'} | {'C3': 'C1', 'C1': 'C3', 'C2': 'C2'} | Good Credit | {'C1': 'Good Credit', 'C3': 'Bad Credit', 'C2': 'Other'} |
SVC | C1 | Vehicle Insurance Claims | First of all, the classification decision is solely based on the information or data supplied to the prediction model. According to the model, there is a 61.61% chance that C1 is the true label, and a 38.39% chance that C2 is the true label. Since the predicted probability of C1 is higher than that of C2, it is valid t... | [
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"neg... | 43 | 400 | {'C1': '61.61%', 'C2': '38.39%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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"F15",
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"F32",
"F7",
"F24",
"F29",
"F18",
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] | {'F26': 'incident_severity', 'F13': 'insured_hobbies', 'F9': 'authorities_contacted', 'F27': 'insured_education_level', 'F4': 'umbrella_limit', 'F17': 'insured_relationship', 'F20': 'auto_make', 'F16': 'insured_occupation', 'F10': 'capital-gains', 'F11': 'policy_deductable', 'F31': 'policy_state', 'F25': 'auto_year', '... | {'F27': 'F26', 'F23': 'F13', 'F28': 'F9', 'F21': 'F27', 'F5': 'F4', 'F24': 'F17', 'F33': 'F20', 'F22': 'F16', 'F7': 'F10', 'F3': 'F11', 'F18': 'F31', 'F17': 'F25', 'F20': 'F19', 'F16': 'F22', 'F30': 'F30', 'F10': 'F21', 'F6': 'F6', 'F14': 'F2', 'F15': 'F8', 'F25': 'F1', 'F13': 'F12', 'F32': 'F33', 'F31': 'F3', 'F29': '... | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
GradientBoostingClassifier | C2 | Paris House Classification | Because the prediction probability of C1 is barely 0.70 percent, the classifier outputs the label C2 with near 100 percent confidence based on the values of the input attributes. The effects of F13, F10, and F14 on the aforementioned classification decision are significant. The values of these features are given greate... | [
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"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative"
] | 154 | 224 | {'C2': '99.30%', 'C1': '0.70%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F10, F9, F11 and F7) on the model’s prediction of C2.",
"Summarize the set... | [
"F13",
"F14",
"F10",
"F9",
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"F7",
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"F12",
"F15",
"F6",
"F4",
"F8",
"F5",
"F2",
"F17",
"F3",
"F1"
] | {'F13': 'isNewBuilt', 'F14': 'hasYard', 'F10': 'hasPool', 'F9': 'hasStormProtector', 'F11': 'made', 'F7': 'hasGuestRoom', 'F16': 'squareMeters', 'F12': 'floors', 'F15': 'cityCode', 'F6': 'basement', 'F4': 'price', 'F8': 'numPrevOwners', 'F5': 'numberOfRooms', 'F2': 'attic', 'F17': 'cityPartRange', 'F3': 'garage', 'F1':... | {'F3': 'F13', 'F1': 'F14', 'F2': 'F10', 'F4': 'F9', 'F12': 'F11', 'F16': 'F7', 'F6': 'F16', 'F8': 'F12', 'F9': 'F15', 'F13': 'F6', 'F17': 'F4', 'F11': 'F8', 'F7': 'F5', 'F14': 'F2', 'F10': 'F17', 'F15': 'F3', 'F5': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Basic | {'C2': 'Basic', 'C1': 'Luxury'} |
SGDClassifier | C3 | Flight Price-Range Classification | The classification algorithm arrived at the prediction output based on the variables or information supplied about the case under consideration. The prediction probabilities across the three-class labels, C2, C3, and C1, respectively, are 28.17%, 50.21%, and 21.62%, making C3 the label assigned by the algorithm, judged... | [
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"positive",
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"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative",
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] | 443 | 467 | {'C2': '28.17%', 'C3': '50.21%', 'C1': '21.62%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F8",
"F2",
"F10",
"F3",
"F1",
"F9",
"F5",
"F4",
"F12",
"F11",
"F6",
"F7"
] | {'F8': 'Airline', 'F2': 'Total_Stops', 'F10': 'Arrival_minute', 'F3': 'Journey_day', 'F1': 'Dep_hour', 'F9': 'Source', 'F5': 'Dep_minute', 'F4': 'Duration_hours', 'F12': 'Destination', 'F11': 'Journey_month', 'F6': 'Duration_mins', 'F7': 'Arrival_hour'} | {'F9': 'F8', 'F12': 'F2', 'F6': 'F10', 'F1': 'F3', 'F3': 'F1', 'F10': 'F9', 'F4': 'F5', 'F7': 'F4', 'F11': 'F12', 'F2': 'F11', 'F8': 'F6', 'F5': 'F7'} | {'C2': 'C2', 'C3': 'C3', 'C1': 'C1'} | Moderate | {'C2': 'Low', 'C3': 'Moderate', 'C1': 'High'} |
RandomForestClassifier | C2 | Paris House Classification | Judging based on the information provided on the case under consideration, the model outputs that the prediction probability of C1 is only 0.48%, indicating that with about 99.52% certainty, the true label here is C2 and in simple terms, the model is very confident that the true label for the case under consideration i... | [
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"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative"
] | 441 | 205 | {'C2': '99.52%', 'C1': '0.48%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F17",
"F13",
"F10",
"F1",
"F3",
"F11",
"F16",
"F7",
"F9",
"F8",
"F15",
"F6",
"F2",
"F4",
"F14",
"F5",
"F12"
] | {'F17': 'isNewBuilt', 'F13': 'hasYard', 'F10': 'hasPool', 'F1': 'made', 'F3': 'hasStormProtector', 'F11': 'hasGuestRoom', 'F16': 'squareMeters', 'F7': 'floors', 'F9': 'price', 'F8': 'cityCode', 'F15': 'basement', 'F6': 'numPrevOwners', 'F2': 'cityPartRange', 'F4': 'numberOfRooms', 'F14': 'attic', 'F5': 'garage', 'F12':... | {'F3': 'F17', 'F1': 'F13', 'F2': 'F10', 'F12': 'F1', 'F4': 'F3', 'F16': 'F11', 'F6': 'F16', 'F8': 'F7', 'F17': 'F9', 'F9': 'F8', 'F13': 'F15', 'F11': 'F6', 'F10': 'F2', 'F7': 'F4', 'F14': 'F14', 'F15': 'F5', 'F5': 'F12'} | {'C1': 'C2', 'C2': 'C1'} | Basic | {'C2': 'Basic', 'C1': 'Luxury'} |
GradientBoostingClassifier | C2 | Basketball Players Career Length Prediction | Judging based on the values of the variables passed to the model with respect to the case under consideration, the output labelling decision is as follows: there is about an 83.98% chance that C2 is the correct label, whereas the likelihood of C1 is only 16.02%, hence the label choice with a higher confidence level is ... | [
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"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
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] | 11 | 367 | {'C2': '83.98%', 'C1': '16.02%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F8, F5, F19 and F14) on the model’s prediction of C2.",
"Summarize the set... | [
"F13",
"F1",
"F8",
"F5",
"F19",
"F14",
"F9",
"F15",
"F3",
"F17",
"F12",
"F11",
"F10",
"F16",
"F6",
"F7",
"F18",
"F2",
"F4"
] | {'F13': 'GamesPlayed', 'F1': 'OffensiveRebounds', 'F8': 'FreeThrowPercent', 'F5': 'FieldGoalPercent', 'F19': '3PointPercent', 'F14': '3PointAttempt', 'F9': 'FieldGoalsMade', 'F15': 'Blocks', 'F3': 'DefensiveRebounds', 'F17': 'Turnovers', 'F12': 'Rebounds', 'F11': 'MinutesPlayed', 'F10': 'FreeThrowAttempt', 'F16': 'Assi... | {'F1': 'F13', 'F13': 'F1', 'F12': 'F8', 'F6': 'F5', 'F9': 'F19', 'F8': 'F14', 'F4': 'F9', 'F18': 'F15', 'F14': 'F3', 'F19': 'F17', 'F15': 'F12', 'F2': 'F11', 'F11': 'F10', 'F16': 'F16', 'F7': 'F6', 'F5': 'F7', 'F3': 'F18', 'F17': 'F2', 'F10': 'F4'} | {'C2': 'C2', 'C1': 'C1'} | More than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
RandomForestClassifier | C3 | Mobile Price-Range Classification | The model predicts the class label C3 for the given test instance with a likelihood of about 69.23%. However, there is about a 30.77% chance that the true class label is C2, while the others, C1 and C4, have a 0.0% likelihood. The top features contributing to this prediction decision are F8, F14, F12, and F6, whereas t... | [
"0.50",
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"-0.01",
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] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
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"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 76 | 424 | {'C1': '0.00%', 'C3': '69.23%', 'C2': '30.77%', 'C4': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F8",
"F14",
"F12",
"F6",
"F19",
"F15",
"F16",
"F7",
"F1",
"F13",
"F5",
"F9",
"F4",
"F18",
"F10",
"F17",
"F2",
"F3",
"F11",
"F20"
] | {'F8': 'ram', 'F14': 'touch_screen', 'F12': 'int_memory', 'F6': 'battery_power', 'F19': 'mobile_wt', 'F15': 'sc_w', 'F16': 'four_g', 'F7': 'talk_time', 'F1': 'sc_h', 'F13': 'wifi', 'F5': 'fc', 'F9': 'three_g', 'F4': 'dual_sim', 'F18': 'n_cores', 'F10': 'px_height', 'F17': 'blue', 'F2': 'clock_speed', 'F3': 'px_width', ... | {'F11': 'F8', 'F19': 'F14', 'F4': 'F12', 'F1': 'F6', 'F6': 'F19', 'F13': 'F15', 'F17': 'F16', 'F14': 'F7', 'F12': 'F1', 'F20': 'F13', 'F3': 'F5', 'F18': 'F9', 'F16': 'F4', 'F7': 'F18', 'F9': 'F10', 'F15': 'F17', 'F2': 'F2', 'F10': 'F3', 'F5': 'F11', 'F8': 'F20'} | {'C3': 'C1', 'C4': 'C3', 'C2': 'C2', 'C1': 'C4'} | r2 | {'C1': 'r1', 'C3': 'r2', 'C2': 'r3', 'C4': 'r4'} |
KNeighborsClassifier | C1 | Water Quality Classification | The given case is likely C1 with a confidence level of 87.50% judged based on the values of the input features supplied to the classifier and according to the attributions analysis, F1 and F5 have a high degree of impact. F8, F6, F2, F4, and F9 have a moderate degree of impact while on the contrary F7 and F3 have littl... | [
"0.03",
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"0.01",
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"0.01",
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"-0.00",
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] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative"
] | 51 | 19 | {'C1': '87.50%', 'C2': '12.50%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F1, F5, F8 and F6) on the prediction made for this test case.",
"Compare the direction of impact of the features: F2, F9 and F4.",
"Describe the degree of ... | [
"F1",
"F5",
"F8",
"F6",
"F2",
"F9",
"F4",
"F7",
"F3"
] | {'F1': 'Hardness', 'F5': 'Sulfate', 'F8': 'Solids', 'F6': 'ph', 'F2': 'Organic_carbon', 'F9': 'Conductivity', 'F4': 'Trihalomethanes', 'F7': 'Turbidity', 'F3': 'Chloramines'} | {'F2': 'F1', 'F5': 'F5', 'F3': 'F8', 'F1': 'F6', 'F7': 'F2', 'F6': 'F9', 'F8': 'F4', 'F9': 'F7', 'F4': 'F3'} | {'C2': 'C1', 'C1': 'C2'} | Not Portable | {'C1': 'Not Portable', 'C2': 'Portable'} |
RandomForestClassifier | C3 | Mobile Price-Range Classification | The label for this example is estimated to be C3 among the four possible classes, with a 73.08 percent chance of being true. C2 is the next most likely label, with a probability of roughly 26.92 percent. The above prediction assessment is mostly dependent on the values of the variables F18, F7, F14, F19, and F8. F18 ha... | [
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"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive"
] | 130 | 305 | {'C3': '73.08%', 'C2': '26.92%', 'C4': '0.00%', 'C1': '0.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F18",
"F14",
"F7",
"F8",
"F19",
"F6",
"F16",
"F2",
"F9",
"F1",
"F13",
"F3",
"F11",
"F5",
"F15",
"F17",
"F12",
"F10",
"F20",
"F4"
] | {'F18': 'ram', 'F14': 'px_width', 'F7': 'battery_power', 'F8': 'px_height', 'F19': 'n_cores', 'F6': 'dual_sim', 'F16': 'touch_screen', 'F2': 'int_memory', 'F9': 'wifi', 'F1': 'fc', 'F13': 'four_g', 'F3': 'm_dep', 'F11': 'pc', 'F5': 'mobile_wt', 'F15': 'talk_time', 'F17': 'three_g', 'F12': 'sc_h', 'F10': 'sc_w', 'F20': ... | {'F11': 'F18', 'F10': 'F14', 'F1': 'F7', 'F9': 'F8', 'F7': 'F19', 'F16': 'F6', 'F19': 'F16', 'F4': 'F2', 'F20': 'F9', 'F3': 'F1', 'F17': 'F13', 'F5': 'F3', 'F8': 'F11', 'F6': 'F5', 'F14': 'F15', 'F18': 'F17', 'F12': 'F12', 'F13': 'F10', 'F15': 'F20', 'F2': 'F4'} | {'C2': 'C3', 'C1': 'C2', 'C4': 'C4', 'C3': 'C1'} | r1 | {'C3': 'r1', 'C2': 'r2', 'C4': 'r3', 'C1': 'r4'} |
BernoulliNB | C1 | Personal Loan Modelling | The model has classified the instance as C1 due to the effects of the following features: F2, F7, F4, and F6. Based on the values of these variables, the likelihood of the C1 label is 65.51 percent. F6 and F4 are the top positively contributing variables, whereas F2 and F7 are the most adversely contributing variables.... | [
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] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
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] | 135 | 296 | {'C2': '34.49%', 'C1': '65.51%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F6, F4 and F2) on the prediction made for this test case.",
"Compare the direction of impact of the features: F7, F3 and F9.",
"Describe the degree of impa... | [
"F6",
"F4",
"F2",
"F7",
"F3",
"F9",
"F8",
"F5",
"F1"
] | {'F6': 'CD Account', 'F4': 'Income', 'F2': 'CCAvg', 'F7': 'Securities Account', 'F3': 'Education', 'F9': 'Mortgage', 'F8': 'Age', 'F5': 'Family', 'F1': 'Extra_service'} | {'F8': 'F6', 'F2': 'F4', 'F4': 'F2', 'F7': 'F7', 'F5': 'F3', 'F6': 'F9', 'F1': 'F8', 'F3': 'F5', 'F9': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Accept | {'C2': 'Reject', 'C1': 'Accept'} |
DecisionTreeClassifier | C1 | Insurance Churn | Considering the predicted likelihoods across the classes, C1 is confidently chosen as the true label since its likelihood is 93.27%, implying that the likelihood of C2 is only about 6.73%. F13 and F3 are the two features with a very strong positive influence, favouring the prediction of class C1. The following feature... | [
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"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 83 | 284 | {'C2': '6.73%', 'C1': '93.27%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F13",
"F3",
"F7",
"F10",
"F11",
"F5",
"F8",
"F9",
"F2",
"F12",
"F6",
"F4",
"F14",
"F15",
"F1",
"F16"
] | {'F13': 'feature15', 'F3': 'feature14', 'F7': 'feature10', 'F10': 'feature11', 'F11': 'feature5', 'F5': 'feature13', 'F8': 'feature4', 'F9': 'feature3', 'F2': 'feature12', 'F12': 'feature1', 'F6': 'feature7', 'F4': 'feature2', 'F14': 'feature6', 'F15': 'feature0', 'F1': 'feature9', 'F16': 'feature8'} | {'F9': 'F13', 'F8': 'F3', 'F4': 'F7', 'F5': 'F10', 'F15': 'F11', 'F7': 'F5', 'F14': 'F8', 'F13': 'F9', 'F6': 'F2', 'F11': 'F12', 'F1': 'F6', 'F12': 'F4', 'F16': 'F14', 'F10': 'F15', 'F3': 'F1', 'F2': 'F16'} | {'C1': 'C2', 'C2': 'C1'} | Leave | {'C2': 'Stay', 'C1': 'Leave'} |
GradientBoostingClassifier | C1 | Basketball Players Career Length Prediction | The classification output is C1, however, the classifier is somewhat unsure about this prediction decision because the corresponding predicted probability is only 55.19%. F3 is by far the most influential feature whereas F1, F13, and F14 have been recognised as having the biggest effect on prediction output here after ... | [
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"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
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"negative"
] | 88 | 268 | {'C2': '44.81%', 'C1': '55.19%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F3, F1, F13, F14 and F10.",
... | [
"F3",
"F1",
"F13",
"F14",
"F10",
"F9",
"F7",
"F2",
"F5",
"F11",
"F15",
"F17",
"F4",
"F6",
"F19",
"F8",
"F12",
"F18",
"F16"
] | {'F3': 'GamesPlayed', 'F1': 'OffensiveRebounds', 'F13': 'FieldGoalPercent', 'F14': 'FreeThrowPercent', 'F10': '3PointPercent', 'F9': '3PointAttempt', 'F7': 'FieldGoalsMade', 'F2': 'Blocks', 'F5': 'DefensiveRebounds', 'F11': 'Turnovers', 'F15': 'Rebounds', 'F17': 'MinutesPlayed', 'F4': 'FreeThrowAttempt', 'F6': '3PointM... | {'F1': 'F3', 'F13': 'F1', 'F6': 'F13', 'F12': 'F14', 'F9': 'F10', 'F8': 'F9', 'F4': 'F7', 'F18': 'F2', 'F14': 'F5', 'F19': 'F11', 'F15': 'F15', 'F2': 'F17', 'F11': 'F4', 'F7': 'F6', 'F16': 'F19', 'F3': 'F8', 'F10': 'F12', 'F5': 'F18', 'F17': 'F16'} | {'C2': 'C2', 'C1': 'C1'} | Less than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
LogisticRegression | C1 | Customer Churn Modelling | Judging based on the values of the input variables, the classification algorithm labels the case as C1 since its prediction likelihood is equal to 88.69%. The prediction decision is primarily based on the contributions of F7, F4, and F10, however, F6, F1, and F9 are shown to be the least important variables. Regardin... | [
"0.15",
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"-0.11",
"-0.07",
"-0.02",
"-0.02",
"0.01",
"0.01",
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] | [
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative"
] | 335 | 188 | {'C1': '88.69%', 'C2': '11.31%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F7",
"F10",
"F4",
"F3",
"F8",
"F5",
"F2",
"F6",
"F1",
"F9"
] | {'F7': 'IsActiveMember', 'F10': 'NumOfProducts', 'F4': 'Geography', 'F3': 'Gender', 'F8': 'Age', 'F5': 'CreditScore', 'F2': 'EstimatedSalary', 'F6': 'Balance', 'F1': 'Tenure', 'F9': 'HasCrCard'} | {'F9': 'F7', 'F7': 'F10', 'F2': 'F4', 'F3': 'F3', 'F4': 'F8', 'F1': 'F5', 'F10': 'F2', 'F6': 'F6', 'F5': 'F1', 'F8': 'F9'} | {'C2': 'C1', 'C1': 'C2'} | Stay | {'C1': 'Stay', 'C2': 'Leave'} |
BernoulliNB | C2 | Water Quality Classification | The classification algorithm predicts class C2 with a confidence level of 61.55% and this implies that the probability of the alternative label is only 38.45%. In this case, the top features driving the prediction decision are F7, F8, F2, and F4, followed by F3, F5, F9, F6, and finally F1. Based on the inspections perf... | [
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"-0.03",
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"0.01",
"0.01",
"-0.00",
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] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative"
] | 101 | 417 | {'C2': '61.55%', 'C1': '38.45%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F4, F3 and F5) on the model’s prediction of C2.",
"Summarize the set of fe... | [
"F7",
"F8",
"F2",
"F4",
"F3",
"F5",
"F9",
"F6",
"F1"
] | {'F7': 'Sulfate', 'F8': 'ph', 'F2': 'Trihalomethanes', 'F4': 'Chloramines', 'F3': 'Organic_carbon', 'F5': 'Hardness', 'F9': 'Solids', 'F6': 'Turbidity', 'F1': 'Conductivity'} | {'F5': 'F7', 'F1': 'F8', 'F8': 'F2', 'F4': 'F4', 'F7': 'F3', 'F2': 'F5', 'F3': 'F9', 'F9': 'F6', 'F6': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Not Portable | {'C2': 'Not Portable', 'C1': 'Portable'} |
RandomForestClassifier | C2 | Flight Price-Range Classification | The classification model's decision about the true label for the case is based on the information provided to it. Among the three labels, C2, C1, and C3, the model shows without a doubt that neither C1 nor C3 is the true label, given that the probability of C2 being the true label is 100.0%. F4, F5, and F8 are the main... | [
"0.23",
"0.19",
"0.17",
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"0.03",
"0.02",
"-0.02",
"0.02",
"0.01",
"-0.01",
"0.01",
"0.01"
] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive"
] | 436 | 203 | {'C2': '100.00%', 'C1': '0.00%', 'C3': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F4",
"F5",
"F8",
"F9",
"F6",
"F3",
"F7",
"F11",
"F2",
"F10",
"F1",
"F12"
] | {'F4': 'Duration_hours', 'F5': 'Airline', 'F8': 'Total_Stops', 'F9': 'Journey_day', 'F6': 'Source', 'F3': 'Duration_mins', 'F7': 'Arrival_hour', 'F11': 'Destination', 'F2': 'Arrival_minute', 'F10': 'Dep_minute', 'F1': 'Journey_month', 'F12': 'Dep_hour'} | {'F7': 'F4', 'F9': 'F5', 'F12': 'F8', 'F1': 'F9', 'F10': 'F6', 'F8': 'F3', 'F5': 'F7', 'F11': 'F11', 'F6': 'F2', 'F4': 'F10', 'F2': 'F1', 'F3': 'F12'} | {'C2': 'C2', 'C3': 'C1', 'C1': 'C3'} | Low | {'C2': 'Low', 'C1': 'Moderate', 'C3': 'High'} |
LogisticRegression | C2 | Basketball Players Career Length Prediction | According to the model, C2 is the class with the higher probability, which is equal to 52.57 percent, of being the label for this selected instance or case. Conversely, there is a 47.43 percent chance that C1 is the correct label showing that the model is less certain about the classification verdict in this case. This... | [
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"-0.10",
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"-0.01",
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] | [
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative"
] | 165 | 91 | {'C1': '47.43%', 'C2': '52.57%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F19",
"F7",
"F11",
"F1",
"F10",
"F9",
"F15",
"F14",
"F6",
"F5",
"F16",
"F12",
"F3",
"F13",
"F2",
"F17",
"F4",
"F18",
"F8"
] | {'F19': '3PointMade', 'F7': '3PointAttempt', 'F11': 'FreeThrowMade', 'F1': 'FreeThrowAttempt', 'F10': 'GamesPlayed', 'F9': 'OffensiveRebounds', 'F15': 'FieldGoalsAttempt', 'F14': 'DefensiveRebounds', 'F6': 'Assists', 'F5': 'MinutesPlayed', 'F16': 'FieldGoalsMade', 'F12': 'Blocks', 'F3': 'Rebounds', 'F13': 'FieldGoalPer... | {'F7': 'F19', 'F8': 'F7', 'F10': 'F11', 'F11': 'F1', 'F1': 'F10', 'F13': 'F9', 'F5': 'F15', 'F14': 'F14', 'F16': 'F6', 'F2': 'F5', 'F4': 'F16', 'F18': 'F12', 'F15': 'F3', 'F6': 'F13', 'F17': 'F2', 'F3': 'F17', 'F12': 'F4', 'F19': 'F18', 'F9': 'F8'} | {'C2': 'C1', 'C1': 'C2'} | Less than 5 | {'C1': 'More than 5', 'C2': 'Less than 5'} |
RandomForestClassifier | C2 | Printer Sales | According to the predicted likelihoods across the classes, C1 has a 17.0% chance of being the true label for the given data or case, implying that C2 is the most likely label. F16, F14, and F12 are the most important factors that led to the classification judgments above. The remaining factors have a minor or non-exist... | [
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"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"negligible",
"negligible",
"neg... | 240 | 322 | {'C2': '83.00%', 'C1': '17.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F16",
"F14",
"F12",
"F10",
"F23",
"F7",
"F3",
"F4",
"F6",
"F25",
"F19",
"F9",
"F20",
"F18",
"F21",
"F13",
"F5",
"F26",
"F17",
"F24",
"F2",
"F1",
"F11",
"F22",
"F8",
"F15"
] | {'F16': 'X8', 'F14': 'X24', 'F12': 'X1', 'F10': 'X2', 'F23': 'X10', 'F7': 'X15', 'F3': 'X25', 'F4': 'X23', 'F6': 'X18', 'F25': 'X4', 'F19': 'X7', 'F9': 'X17', 'F20': 'X3', 'F18': 'X22', 'F21': 'X5', 'F13': 'X9', 'F5': 'X12', 'F26': 'X19', 'F17': 'X11', 'F24': 'X16', 'F2': 'X14', 'F1': 'X21', 'F11': 'X20', 'F22': 'X13',... | {'F8': 'F16', 'F24': 'F14', 'F1': 'F12', 'F2': 'F10', 'F10': 'F23', 'F15': 'F7', 'F25': 'F3', 'F23': 'F4', 'F18': 'F6', 'F4': 'F25', 'F7': 'F19', 'F17': 'F9', 'F3': 'F20', 'F22': 'F18', 'F5': 'F21', 'F9': 'F13', 'F12': 'F5', 'F19': 'F26', 'F11': 'F17', 'F16': 'F24', 'F14': 'F2', 'F21': 'F1', 'F20': 'F11', 'F13': 'F22',... | {'C2': 'C2', 'C1': 'C1'} | Less | {'C2': 'Less', 'C1': 'More'} |
RandomForestClassifier | C1 | Credit Risk Classification | According to the ML model, C1 is the most likely class label, and we can conclude that the model is quite confident about the decision given that the probability of having C2 as the correct label is only 7.0%. For the case under study, analysis indicates that F10, F4, F6, and F7 are essentially the negative set of feat... | [
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] | [
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 182 | 287 | {'C1': '93.00%', 'C2': '7.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F2",
"F6",
"F11",
"F10",
"F3",
"F9",
"F4",
"F7",
"F8",
"F1",
"F5"
] | {'F2': 'fea_4', 'F6': 'fea_10', 'F11': 'fea_8', 'F10': 'fea_7', 'F3': 'fea_2', 'F9': 'fea_3', 'F4': 'fea_5', 'F7': 'fea_1', 'F8': 'fea_9', 'F1': 'fea_6', 'F5': 'fea_11'} | {'F4': 'F2', 'F10': 'F6', 'F8': 'F11', 'F7': 'F10', 'F2': 'F3', 'F3': 'F9', 'F5': 'F4', 'F1': 'F7', 'F9': 'F8', 'F6': 'F1', 'F11': 'F5'} | {'C1': 'C1', 'C2': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
MLPClassifier | C1 | Annual Income Earnings | Because the confidence level associated with the other class, C2, is just 2.29%, the model predicts that the given example is likely C1 and to be specific, the model is quite certain that the right label for the given case is C1. All the features are shown to have some degree of influence on the decision above, with F9... | [
"0.62",
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] | [
"positive",
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"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative"
] | 201 | 116 | {'C2': '2.29%', 'C1': '97.71%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F12, F11, F7, F2 and F6.",
"Compare and contrast the impact of the following features (F13, F4 and F5) on the model’s prediction of C1.",
"D... | [
"F12",
"F11",
"F7",
"F2",
"F6",
"F13",
"F4",
"F5",
"F3",
"F1",
"F14",
"F8",
"F9",
"F10"
] | {'F12': 'Capital Gain', 'F11': 'Marital Status', 'F7': 'Capital Loss', 'F2': 'Relationship', 'F6': 'Hours per week', 'F13': 'Education', 'F4': 'Country', 'F5': 'Age', 'F3': 'Occupation', 'F1': 'Sex', 'F14': 'Education-Num', 'F8': 'Workclass', 'F9': 'fnlwgt', 'F10': 'Race'} | {'F11': 'F12', 'F6': 'F11', 'F12': 'F7', 'F8': 'F2', 'F13': 'F6', 'F4': 'F13', 'F14': 'F4', 'F1': 'F5', 'F7': 'F3', 'F10': 'F1', 'F5': 'F14', 'F2': 'F8', 'F3': 'F9', 'F9': 'F10'} | {'C1': 'C2', 'C2': 'C1'} | Above 50K | {'C2': 'Under 50K', 'C1': 'Above 50K'} |
KNNClassifier | C2 | Car Acceptability Valuation | The classifier made the prediction here based on the information provided about the case under consideration, and according to the classifier, the prediction probabilities or likelihoods across the labels C2 and C1 are 100.0% and 0.0%, respectively. All the input features are shown to have different degrees of influenc... | [
"0.34",
"0.33",
"-0.13",
"-0.12",
"0.06",
"0.04"
] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive"
] | 435 | 462 | {'C2': '100.00%', 'C1': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F2",
"F3",
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] | {'F2': 'persons', 'F3': 'safety', 'F4': 'lug_boot', 'F1': 'buying', 'F6': 'doors', 'F5': 'maint'} | {'F4': 'F2', 'F6': 'F3', 'F5': 'F4', 'F1': 'F1', 'F3': 'F6', 'F2': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Unacceptable | {'C2': 'Unacceptable', 'C1': 'Acceptable'} |
LogisticRegression | C1 | Real Estate Investment | For the selected case, the model assigns the label C1. The prediction probability distribution across the classes C2 and C1 is 2.40% and 97.60%, respectively. The most important features considered for this prediction are F6, F12, F18, and F1, while on the other hand, the least relevant features with little contributio... | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F6, F12 and F18.",
"Summariz... | [
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MLPClassifier | C1 | Vehicle Insurance Claims | The given instance was labelled as C1 by the model based on the values of its features. The model is about 79.64% certain about this prediction decision, hence, there is a slight chance that the label could be C2. Among the different features, the ones with the most impact on the model are F6, F17, F25, F11, and F13. T... | [
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"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F6 (value equal to V0), F17 (value equal to V15), F25 (value equal to V2), F11 and F13 (equal to V0).",
"Compare and contrast the impact of... | [
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] | {'F6': 'incident_severity', 'F17': 'insured_hobbies', 'F25': 'insured_relationship', 'F11': 'umbrella_limit', 'F13': 'insured_education_level', 'F33': 'authorities_contacted', 'F24': 'incident_type', 'F1': 'policy_csl', 'F14': 'number_of_vehicles_involved', 'F19': 'capital-loss', 'F8': 'property_damage', 'F21': 'insure... | {'F27': 'F6', 'F23': 'F17', 'F24': 'F25', 'F5': 'F11', 'F21': 'F13', 'F28': 'F33', 'F25': 'F24', 'F19': 'F1', 'F10': 'F14', 'F8': 'F19', 'F31': 'F8', 'F22': 'F21', 'F2': 'F22', 'F29': 'F7', 'F6': 'F31', 'F26': 'F2', 'F15': 'F12', 'F14': 'F29', 'F7': 'F30', 'F12': 'F16', 'F30': 'F10', 'F32': 'F4', 'F1': 'F32', 'F17': 'F... | {'C1': 'C1', 'C2': 'C2'} | Not Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
RandomForestClassifier | C1 | Ethereum Fraud Detection | According to the classification algorithm, the best label for the given case is C1, because there is little to no chance that C2 is the correct label. Not all of the features are found to contribute to the label given here. The following significant features are ordered in order of their effect on the algorithm's outpu... | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F15, F8, F29, F26 and F13.",
... | [
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"... | {'F15': ' ERC20 total Ether sent contract', 'F8': ' ERC20 min val rec', 'F29': 'total transactions (including tnx to create contract', 'F26': ' ERC20 max val rec', 'F13': ' Total ERC20 tnxs', 'F28': ' ERC20 uniq rec addr', 'F18': 'min val sent', 'F33': 'Time Diff between first and last (Mins)', 'F19': 'Sent tnx', 'F22'... | {'F26': 'F15', 'F31': 'F8', 'F18': 'F29', 'F32': 'F26', 'F23': 'F13', 'F28': 'F28', 'F12': 'F18', 'F3': 'F33', 'F4': 'F19', 'F2': 'F22', 'F9': 'F24', 'F25': 'F25', 'F14': 'F21', 'F13': 'F6', 'F1': 'F37', 'F5': 'F32', 'F37': 'F9', 'F8': 'F11', 'F38': 'F2', 'F30': 'F3', 'F19': 'F17', 'F6': 'F12', 'F36': 'F31', 'F35': 'F3... | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
BernoulliNB | C1 | Hotel Satisfaction | The classifier labbelled the given case as C1 with a confidence level of 98.89%, implying that the chance of C2 being the correct label is only about 1.11%. The classification output decision is solely based on the information supplied to the classifier about the case under review. We can rank the contributions of the ... | [
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] | 16 | 372 | {'C1': '98.89%', 'C2': '1.11%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F14, F10 and F15) on the model’s prediction of C1.",
"Summarize the set of... | [
"F3",
"F5",
"F8",
"F14",
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"F15",
"F4",
"F7",
"F9",
"F11",
"F6",
"F1",
"F2",
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"F13"
] | {'F3': 'Type of Travel', 'F5': 'Type Of Booking', 'F8': 'Common Room entertainment', 'F14': 'Stay comfort', 'F10': 'Cleanliness', 'F15': 'Hotel wifi service', 'F4': 'Other service', 'F7': 'Ease of Online booking', 'F9': 'Age', 'F11': 'Checkin\\/Checkout service', 'F6': 'Food and drink', 'F1': 'Departure\\/Arrival conv... | {'F3': 'F3', 'F4': 'F5', 'F12': 'F8', 'F11': 'F14', 'F15': 'F10', 'F6': 'F15', 'F14': 'F4', 'F8': 'F7', 'F5': 'F9', 'F13': 'F11', 'F10': 'F6', 'F7': 'F1', 'F2': 'F2', 'F9': 'F12', 'F1': 'F13'} | {'C2': 'C1', 'C1': 'C2'} | dissatisfied | {'C1': 'dissatisfied', 'C2': 'satisfied'} |
RandomForestClassifier | C2 | Used Cars Price-Range Prediction | The prediction probability associated with class C1 is 10.50%, while that of class C2 is 89.50%, therefore, it can be concluded that C2 is the most probable label for the given case according to the model. All the input features are shown to contribute to the above decision, and the ones with the strongest influence on... | [
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"positive",
"positive",
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] | 259 | 169 | {'C1': '10.50%', 'C2': '89.50%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F7",
"F2",
"F8",
"F4",
"F6",
"F10",
"F1",
"F9",
"F5",
"F3"
] | {'F7': 'Power', 'F2': 'car_age', 'F8': 'Transmission', 'F4': 'Fuel_Type', 'F6': 'Name', 'F10': 'Mileage', 'F1': 'Engine', 'F9': 'Owner_Type', 'F5': 'Kilometers_Driven', 'F3': 'Seats'} | {'F4': 'F7', 'F5': 'F2', 'F8': 'F8', 'F7': 'F4', 'F6': 'F6', 'F2': 'F10', 'F3': 'F1', 'F9': 'F9', 'F1': 'F5', 'F10': 'F3'} | {'C1': 'C1', 'C2': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
SVC | C1 | Food Ordering Customer Churn Prediction | The model labels the case as C1 with fairly high confidence equal to 89.73%, whereas the likelihood of C2 is only 10.27%. Analysis shows that only 20 of the 46 input variables contribute to the prediction assertion above. The prediction judgement C1 is mainly based on the variables F38, F29, F30, and F46. F1, F11, F18,... | [
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"neg... | 173 | 219 | {'C1': '89.73%', 'C2': '10.27%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F38 and F29.",
"Summarize th... | [
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... | {'F38': 'Ease and convenient', 'F29': 'Unaffordable', 'F46': 'Good Food quality', 'F30': 'Wrong order delivered', 'F1': 'Delay of delivery person picking up food', 'F11': 'Politeness', 'F18': 'Self Cooking', 'F45': 'Late Delivery', 'F15': 'Health Concern', 'F23': 'More Offers and Discount', 'F40': 'Easy Payment option'... | {'F10': 'F38', 'F23': 'F29', 'F15': 'F46', 'F27': 'F30', 'F26': 'F1', 'F42': 'F11', 'F17': 'F18', 'F19': 'F45', 'F18': 'F15', 'F14': 'F23', 'F13': 'F40', 'F11': 'F39', 'F9': 'F14', 'F2': 'F3', 'F35': 'F10', 'F34': 'F22', 'F45': 'F17', 'F16': 'F8', 'F21': 'F42', 'F3': 'F31', 'F38': 'F16', 'F37': 'F33', 'F36': 'F26', 'F1... | {'C2': 'C1', 'C1': 'C2'} | Return | {'C1': 'Return', 'C2': 'Go Away'} |
MLPClassifier | C2 | Annual Income Earnings | The label predicted for this case is C2 with very high confidence of approximately 97.71% which insinuates that there is a marginal possibility that C1 could be the label. The above classification decision is largely due to the values of F9, F11, F4, and F12. On the other hand, F6 and F3 are less relevant when the mode... | [
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] | 158 | 85 | {'C1': '2.29%', 'C2': '97.71%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F9 and F11.",
"Summarize the... | [
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] | {'F9': 'Capital Gain', 'F11': 'Marital Status', 'F4': 'Capital Loss', 'F12': 'Relationship', 'F7': 'Hours per week', 'F10': 'Education', 'F2': 'Country', 'F14': 'Age', 'F1': 'Occupation', 'F5': 'Sex', 'F8': 'Education-Num', 'F13': 'Workclass', 'F6': 'fnlwgt', 'F3': 'Race'} | {'F11': 'F9', 'F6': 'F11', 'F12': 'F4', 'F8': 'F12', 'F13': 'F7', 'F4': 'F10', 'F14': 'F2', 'F1': 'F14', 'F7': 'F1', 'F10': 'F5', 'F5': 'F8', 'F2': 'F13', 'F3': 'F6', 'F9': 'F3'} | {'C2': 'C1', 'C1': 'C2'} | Above 50K | {'C1': 'Under 50K', 'C2': 'Above 50K'} |
SVM_linear | C1 | Wine Quality Prediction | The likelihood of C1 being the correct label for the selected case or instance is 67.54% according to the classifier. This means, there is a 32.46% chance that C2 could be the label and the classification assertion above is influenced mainly by the variables F11, F7, F1, and F10. On the contrary, F4, F8, and F6 are dee... | [
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] | 176 | 100 | {'C2': '32.46%', 'C1': '67.54%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F11, F7, F1 and F10) on the prediction made for this test case.",
"Compare the direction of impact of the features: F2, F5 and F9.",
"Describe the degree o... | [
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"F7",
"F1",
"F10",
"F2",
"F5",
"F9",
"F3",
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] | {'F11': 'residual sugar', 'F7': 'volatile acidity', 'F1': 'alcohol', 'F10': 'fixed acidity', 'F2': 'chlorides', 'F5': 'sulphates', 'F9': 'citric acid', 'F3': 'free sulfur dioxide', 'F4': 'density', 'F8': 'total sulfur dioxide', 'F6': 'pH'} | {'F4': 'F11', 'F2': 'F7', 'F11': 'F1', 'F1': 'F10', 'F5': 'F2', 'F10': 'F5', 'F3': 'F9', 'F6': 'F3', 'F8': 'F4', 'F7': 'F8', 'F9': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | high quality | {'C2': 'low_quality', 'C1': 'high quality'} |
KNeighborsClassifier | C1 | Credit Risk Classification | The confidence level score with respect to each class label suggests that this case should be labelled as C1. Specifically, there is about an 80.0% chance that C1 is the correct label. However, this implies that there is also about a 20.0% chance that it should be C2. The above prediction decision is based predominantl... | [
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"negative",
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"negative",
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] | 112 | 49 | {'C1': '80.00%', 'C2': '20.00%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F2, F5, F7 and F1) on the prediction made for this test case.",
"Compare the direction of impact of the features: F11, F10 and F4.",
"Describe the degree o... | [
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"F5",
"F7",
"F1",
"F11",
"F10",
"F4",
"F9",
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] | {'F2': 'fea_4', 'F5': 'fea_8', 'F7': 'fea_2', 'F1': 'fea_9', 'F11': 'fea_6', 'F10': 'fea_10', 'F4': 'fea_1', 'F9': 'fea_11', 'F8': 'fea_7', 'F3': 'fea_3', 'F6': 'fea_5'} | {'F4': 'F2', 'F8': 'F5', 'F2': 'F7', 'F9': 'F1', 'F6': 'F11', 'F10': 'F10', 'F1': 'F4', 'F11': 'F9', 'F7': 'F8', 'F3': 'F3', 'F5': 'F6'} | {'C2': 'C1', 'C1': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
BernoulliNB | C2 | Job Change of Data Scientists | The classification algorithm is pretty confident that the correct label for the data under consideration is C2, wowever, it is noteworthy to consider that C1 has about a 15.13% chance of being the correct label. The predicted probability of each label is assigned based on the influence of features such as F8, F5, F11,... | [
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] | 220 | 131 | {'C1': '15.13%', 'C2': '84.87%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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] | {'F8': 'city', 'F5': 'enrolled_university', 'F11': 'relevent_experience', 'F4': 'city_development_index', 'F10': 'experience', 'F9': 'education_level', 'F1': 'major_discipline', 'F6': 'last_new_job', 'F12': 'gender', 'F7': 'company_size', 'F2': 'company_type', 'F3': 'training_hours'} | {'F3': 'F8', 'F6': 'F5', 'F5': 'F11', 'F1': 'F4', 'F9': 'F10', 'F7': 'F9', 'F8': 'F1', 'F12': 'F6', 'F4': 'F12', 'F10': 'F7', 'F11': 'F2', 'F2': 'F3'} | {'C2': 'C1', 'C1': 'C2'} | Leave | {'C1': 'Stay', 'C2': 'Leave'} |
GradientBoostingClassifier | C2 | Printer Sales | The case, despite having features with considerable negative impact, also has numerous and measurable positive features, so the assignment of the label C2 by the model is very likely since the predicted probability is 91.95% which is very higher than that of C1. The F20, F13, and F7 were the most important features dri... | [
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"neg... | 111 | 48 | {'C1': '8.05%', 'C2': '91.95%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F23, F11 and F26) on the model’s prediction of C2.",
"Summarize the set of... | [
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] | {'F20': 'X24', 'F13': 'X8', 'F7': 'X1', 'F24': 'X21', 'F22': 'X4', 'F23': 'X6', 'F11': 'X3', 'F26': 'X22', 'F2': 'X7', 'F10': 'X15', 'F1': 'X20', 'F5': 'X11', 'F25': 'X10', 'F8': 'X19', 'F14': 'X5', 'F18': 'X16', 'F15': 'X23', 'F6': 'X9', 'F19': 'X17', 'F9': 'X18', 'F21': 'X25', 'F16': 'X14', 'F12': 'X2', 'F4': 'X13', ... | {'F24': 'F20', 'F8': 'F13', 'F1': 'F7', 'F21': 'F24', 'F4': 'F22', 'F6': 'F23', 'F3': 'F11', 'F22': 'F26', 'F7': 'F2', 'F15': 'F10', 'F20': 'F1', 'F11': 'F5', 'F10': 'F25', 'F19': 'F8', 'F5': 'F14', 'F16': 'F18', 'F23': 'F15', 'F9': 'F6', 'F17': 'F19', 'F18': 'F9', 'F25': 'F21', 'F14': 'F16', 'F2': 'F12', 'F13': 'F4', ... | {'C1': 'C1', 'C2': 'C2'} | More | {'C1': 'Less', 'C2': 'More'} |
LogisticRegression | C1 | Cab Surge Pricing System | The predicted label is C1 given the predictability of C2 is 28.96% and that of C3 is 23.41%. Considering the probabilities of the classes, the model can be described as being moderately confident. The prediction of C1 can be attributed to the varying degree of contributions of the input features. Attribution analysis ... | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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] | {'F4': 'Type_of_Cab', 'F11': 'Confidence_Life_Style_Index', 'F8': 'Destination_Type', 'F9': 'Trip_Distance', 'F1': 'Cancellation_Last_1Month', 'F5': 'Life_Style_Index', 'F10': 'Customer_Rating', 'F7': 'Var3', 'F2': 'Var1', 'F6': 'Customer_Since_Months', 'F12': 'Var2', 'F3': 'Gender'} | {'F2': 'F4', 'F5': 'F11', 'F6': 'F8', 'F1': 'F9', 'F8': 'F1', 'F4': 'F5', 'F7': 'F10', 'F11': 'F7', 'F9': 'F2', 'F3': 'F6', 'F10': 'F12', 'F12': 'F3'} | {'C3': 'C2', 'C1': 'C3', 'C2': 'C1'} | C3 | {'C2': 'Low', 'C3': 'Medium', 'C1': 'High'} |
RandomForestClassifier | C1 | Company Bankruptcy Prediction | The model outputs a predicted probability of 2.55% for the C2 label and 97.45% for the C1 label. Judging from above, the most probable class is C1. Hence, C1 is the assigned label by the model, with a very high confidence level. The top features contributing to the prediction assessment above are F42, F13, F37, F20, an... | [
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"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F53, F32 and F48) on the model’s prediction of C1.",
"Summarize the set of... | [
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"F7... | {'F42': " Net Income to Stockholder's Equity", 'F13': ' Total income\\/Total expense', 'F37': ' Borrowing dependency', 'F20': ' Continuous interest rate (after tax)', 'F53': ' Net Value Per Share (B)', 'F32': ' Cash\\/Current Liability', 'F48': ' Net worth\\/Assets', 'F60': ' Fixed Assets Turnover Frequency', 'F7': ' I... | {'F59': 'F42', 'F57': 'F13', 'F3': 'F37', 'F12': 'F20', 'F27': 'F53', 'F32': 'F32', 'F84': 'F48', 'F22': 'F60', 'F1': 'F7', 'F56': 'F46', 'F42': 'F51', 'F52': 'F79', 'F23': 'F78', 'F83': 'F75', 'F61': 'F19', 'F67': 'F47', 'F60': 'F27', 'F73': 'F66', 'F18': 'F74', 'F79': 'F45', 'F68': 'F65', 'F66': 'F5', 'F62': 'F39', '... | {'C1': 'C2', 'C2': 'C1'} | Yes | {'C2': 'No', 'C1': 'Yes'} |
SVC | C1 | Broadband Sevice Signup | The algorithm identifies the provided data or case as C1 with a greater level of certainty since the prediction probability of class C2 is just 0.07 percent as a result, C2 is less likely than C1. The influence of input features such as F21, F32, F18, F9, and F17 is mostly responsible for the classification verdict abo... | [
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"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F21 and F32.",
"Compare and contrast the impact of the following features (F18, F9, F17 and F34) on the model’s prediction of C1.",
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... | {'F21': 'X38', 'F32': 'X32', 'F18': 'X31', 'F9': 'X25', 'F17': 'X8', 'F34': 'X35', 'F37': 'X1', 'F35': 'X3', 'F23': 'X28', 'F39': 'X19', 'F24': 'X9', 'F40': 'X11', 'F11': 'X10', 'F31': 'X21', 'F27': 'X17', 'F38': 'X4', 'F30': 'X36', 'F3': 'X2', 'F4': 'X6', 'F6': 'X34', 'F16': 'X37', 'F7': 'X40', 'F13': 'X42', 'F28': 'X... | {'F35': 'F21', 'F29': 'F32', 'F28': 'F18', 'F23': 'F9', 'F6': 'F17', 'F32': 'F34', 'F40': 'F37', 'F2': 'F35', 'F26': 'F23', 'F17': 'F39', 'F7': 'F24', 'F9': 'F40', 'F8': 'F11', 'F19': 'F31', 'F15': 'F27', 'F3': 'F38', 'F33': 'F30', 'F1': 'F3', 'F4': 'F4', 'F31': 'F6', 'F34': 'F16', 'F37': 'F7', 'F38': 'F13', 'F39': 'F2... | {'C1': 'C1', 'C2': 'C2'} | No | {'C1': 'No', 'C2': 'Yes'} |
SVM | C2 | Customer Churn Modelling | For the given dataset instance, the label assigned by the classifier is C2 since it has a predicted probability of about 89.16%. On the other hand, there is a 9.0% chance that C1 could be the appropriate label, whereas C3 only has a 1.84% chance of being the true label. The classifier arrived at this classification ver... | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F7",
"F1",
"F3",
"F10",
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"F6",
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] | {'F7': 'IsActiveMember', 'F1': 'Age', 'F3': 'Geography', 'F10': 'NumOfProducts', 'F9': 'Gender', 'F6': 'Tenure', 'F8': 'CreditScore', 'F2': 'Balance', 'F4': 'EstimatedSalary', 'F5': 'HasCrCard'} | {'F9': 'F7', 'F4': 'F1', 'F2': 'F3', 'F7': 'F10', 'F3': 'F9', 'F5': 'F6', 'F1': 'F8', 'F6': 'F2', 'F10': 'F4', 'F8': 'F5'} | {'C1': 'C2', 'C2': 'C1', 'C3': 'C3'} | Stay | {'C2': 'Stay', 'C1': 'Leave', 'C3': 'Other'} |
GradientBoostingClassifier | C2 | Basketball Players Career Length Prediction | The case is labelled as C2 by the model but looking at the predicted probabilities across the different classes, there is a 33.63% chance that the label could be C1. To explain the above prediction conclusion, the analysis revealed that the majority of the features have negative influences or attributions, pushing the ... | [
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] | 150 | 79 | {'C1': '33.63%', 'C2': '66.37%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F5, F4 and F14.",
"Summarize... | [
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"F3",
"F2",
"F13",
"F17",
"F1",
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] | {'F5': 'GamesPlayed', 'F4': 'OffensiveRebounds', 'F14': 'FieldGoalPercent', 'F19': 'FreeThrowPercent', 'F16': '3PointPercent', 'F10': '3PointAttempt', 'F6': 'FieldGoalsMade', 'F7': 'Blocks', 'F12': 'DefensiveRebounds', 'F15': 'Turnovers', 'F8': 'Rebounds', 'F3': 'FreeThrowAttempt', 'F2': 'MinutesPlayed', 'F13': 'Assist... | {'F1': 'F5', 'F13': 'F4', 'F6': 'F14', 'F12': 'F19', 'F9': 'F16', 'F8': 'F10', 'F4': 'F6', 'F18': 'F7', 'F14': 'F12', 'F19': 'F15', 'F15': 'F8', 'F11': 'F3', 'F2': 'F2', 'F16': 'F13', 'F5': 'F17', 'F7': 'F1', 'F3': 'F9', 'F10': 'F11', 'F17': 'F18'} | {'C1': 'C1', 'C2': 'C2'} | Less than 5 | {'C1': 'More than 5', 'C2': 'Less than 5'} |
BernoulliNB | C1 | Customer Churn Modelling | The most likely label chosen by the model in this case is C1. The decision above is based on the prediction probabilities for the two possible labels, C1 and C2, which are 94.25% and 5.75%, respectively. The following variables can be ranked from most important to least important based on their contribution to the mode... | [
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"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
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] | 210 | 256 | {'C1': '94.25%', 'C2': '5.75%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F8, F4, F7, F3 and F1.",
"Su... | [
"F8",
"F4",
"F7",
"F3",
"F1",
"F10",
"F6",
"F5",
"F2",
"F9"
] | {'F8': 'IsActiveMember', 'F4': 'NumOfProducts', 'F7': 'Gender', 'F3': 'Geography', 'F1': 'Age', 'F10': 'CreditScore', 'F6': 'EstimatedSalary', 'F5': 'Balance', 'F2': 'HasCrCard', 'F9': 'Tenure'} | {'F9': 'F8', 'F7': 'F4', 'F3': 'F7', 'F2': 'F3', 'F4': 'F1', 'F1': 'F10', 'F10': 'F6', 'F6': 'F5', 'F8': 'F2', 'F5': 'F9'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Stay', 'C2': 'Leave'} |
BernoulliNB | C2 | Personal Loan Modelling | As per the classification algorithm employed, the most probable label for the data under consideration is C2 since the chances of C1 is very slim and negligible. The main driver behind the labelling decision above is F1. The features with moderate influence are F3, F7, F8, F4, F5, and F9, while those with very small o... | [
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"negative",
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"negative",
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] | 238 | 144 | {'C2': '99.99%', 'C1': '0.01%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F1",
"F3",
"F7",
"F8",
"F4",
"F5",
"F9",
"F6",
"F2"
] | {'F1': 'CD Account', 'F3': 'Income', 'F7': 'CCAvg', 'F8': 'Securities Account', 'F4': 'Education', 'F5': 'Family', 'F9': 'Mortgage', 'F6': 'Age', 'F2': 'Extra_service'} | {'F8': 'F1', 'F2': 'F3', 'F4': 'F7', 'F7': 'F8', 'F5': 'F4', 'F3': 'F5', 'F6': 'F9', 'F1': 'F6', 'F9': 'F2'} | {'C2': 'C2', 'C1': 'C1'} | Reject | {'C2': 'Reject', 'C1': 'Accept'} |
MLPClassifier | C1 | Vehicle Insurance Claims | The ML algorithm classifies the provided data or case as C1 with a likelihood of 80.70%, hinting that the likelihood of C2 being the correct label is only 19.30%. This classification decision above is mainly based on the influence or contributions of the input features. The most relevant features driving the classifica... | [
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"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
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] | {'F17': 'incident_severity', 'F3': 'insured_relationship', 'F11': 'authorities_contacted', 'F10': 'vehicle_claim', 'F15': 'umbrella_limit', 'F13': 'insured_hobbies', 'F28': 'incident_type', 'F31': 'policy_deductable', 'F23': 'auto_make', 'F5': 'number_of_vehicles_involved', 'F29': 'insured_occupation', 'F30': 'property... | {'F27': 'F17', 'F24': 'F3', 'F28': 'F11', 'F16': 'F10', 'F5': 'F15', 'F23': 'F13', 'F25': 'F28', 'F3': 'F31', 'F33': 'F23', 'F10': 'F5', 'F22': 'F29', 'F31': 'F30', 'F29': 'F2', 'F17': 'F18', 'F8': 'F16', 'F19': 'F20', 'F26': 'F33', 'F7': 'F14', 'F15': 'F32', 'F9': 'F7', 'F32': 'F9', 'F4': 'F4', 'F30': 'F19', 'F6': 'F2... | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
SVC | C1 | Broadband Sevice Signup | The predicted probability of class C2 is 12.81% and that of class C1 is 87.19%. Therefore, the label chosen by the model is C1, which is the most probable class. The top two features with significant influence on the prediction verdict above are F2 and F40. These features have positive attributions, shifting the decisi... | [
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"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F2 and F40.",
"Compare and contrast the impact of the following features (F11, F22, F29 and F36) on the model’s prediction of C1.",
"Describ... | [
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... | {'F2': 'X38', 'F40': 'X32', 'F11': 'X22', 'F22': 'X35', 'F29': 'X25', 'F36': 'X16', 'F20': 'X12', 'F7': 'X31', 'F10': 'X3', 'F8': 'X9', 'F3': 'X1', 'F15': 'X19', 'F16': 'X4', 'F5': 'X2', 'F31': 'X29', 'F28': 'X42', 'F13': 'X36', 'F12': 'X21', 'F23': 'X40', 'F1': 'X10', 'F30': 'X33', 'F41': 'X5', 'F39': 'X6', 'F14': 'X4... | {'F35': 'F2', 'F29': 'F40', 'F20': 'F11', 'F32': 'F22', 'F23': 'F29', 'F14': 'F36', 'F10': 'F20', 'F28': 'F7', 'F2': 'F10', 'F7': 'F8', 'F40': 'F3', 'F17': 'F15', 'F3': 'F16', 'F1': 'F5', 'F42': 'F31', 'F38': 'F28', 'F33': 'F13', 'F19': 'F12', 'F37': 'F23', 'F8': 'F1', 'F30': 'F30', 'F41': 'F41', 'F4': 'F39', 'F39': 'F... | {'C2': 'C2', 'C1': 'C1'} | Yes | {'C2': 'No', 'C1': 'Yes'} |
MLPClassifier | C2 | Ethereum Fraud Detection | The C1 has a predicted probability of just 3.10% while that of the C2 is 96.90%, therefore, the most likely class selected by the classifier for the given data is C2. The relevant features contributing to this classification are mainly F25, F19, F16, F31, F7, F15, F12, F6, F17, F22, F18, F24, F8, F33, F20, F14, F32, F2... | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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... | {'F25': 'Unique Received From Addresses', 'F19': ' ERC20 total Ether sent contract', 'F16': 'total ether received', 'F31': 'Sent tnx', 'F7': 'Number of Created Contracts', 'F15': ' ERC20 uniq rec token name', 'F12': ' ERC20 uniq rec contract addr', 'F6': 'max value received ', 'F17': 'total transactions (including tnx ... | {'F7': 'F25', 'F26': 'F19', 'F20': 'F16', 'F4': 'F31', 'F6': 'F7', 'F38': 'F15', 'F30': 'F12', 'F10': 'F6', 'F18': 'F17', 'F29': 'F22', 'F27': 'F18', 'F5': 'F24', 'F11': 'F8', 'F28': 'F33', 'F14': 'F20', 'F9': 'F14', 'F8': 'F32', 'F37': 'F28', 'F2': 'F13', 'F3': 'F5', 'F31': 'F30', 'F32': 'F2', 'F34': 'F3', 'F35': 'F4'... | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
RandomForestClassifier | C2 | Printer Sales | There is only a 17.0% chance that C1 is the correct label which implies that the most probable label for the given data or case is C2 given its predicted likelihood of 83.0%. The main influential features resulting in the classification conclusions above are F3, F1, and F5 whereas the remaining features have either a... | [
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"neg... | 240 | 146 | {'C2': '83.00%', 'C1': '17.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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] | {'F3': 'X8', 'F1': 'X24', 'F5': 'X1', 'F4': 'X2', 'F7': 'X10', 'F11': 'X15', 'F23': 'X25', 'F12': 'X23', 'F6': 'X18', 'F26': 'X4', 'F13': 'X7', 'F10': 'X17', 'F8': 'X3', 'F18': 'X22', 'F15': 'X5', 'F21': 'X9', 'F9': 'X12', 'F14': 'X19', 'F25': 'X11', 'F19': 'X16', 'F20': 'X14', 'F16': 'X21', 'F2': 'X20', 'F24': 'X13', ... | {'F8': 'F3', 'F24': 'F1', 'F1': 'F5', 'F2': 'F4', 'F10': 'F7', 'F15': 'F11', 'F25': 'F23', 'F23': 'F12', 'F18': 'F6', 'F4': 'F26', 'F7': 'F13', 'F17': 'F10', 'F3': 'F8', 'F22': 'F18', 'F5': 'F15', 'F9': 'F21', 'F12': 'F9', 'F19': 'F14', 'F11': 'F25', 'F16': 'F19', 'F14': 'F20', 'F21': 'F16', 'F20': 'F2', 'F13': 'F24', ... | {'C2': 'C2', 'C1': 'C1'} | Less | {'C2': 'Less', 'C1': 'More'} |
RandomForestClassifier | C2 | Student Job Placement | The classification algorithm's decision on the true label for the given case is solely dependent on the information presented to it. Per the algorithm, the accurate label for the case under consideration is most likely C2, and the 12.47% possibility of C1 reflects only a minor uncertainty in the classification algorith... | [
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] | 439 | 465 | {'C1': '12.47%', 'C2': '87.53%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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] | {'F9': 'workex', 'F1': 'specialisation', 'F8': 'hsc_p', 'F5': 'gender', 'F11': 'mba_p', 'F7': 'hsc_s', 'F6': 'ssc_p', 'F2': 'etest_p', 'F4': 'ssc_b', 'F12': 'hsc_b', 'F3': 'degree_t', 'F10': 'degree_p'} | {'F11': 'F9', 'F12': 'F1', 'F2': 'F8', 'F6': 'F5', 'F5': 'F11', 'F9': 'F7', 'F1': 'F6', 'F4': 'F2', 'F7': 'F4', 'F8': 'F12', 'F10': 'F3', 'F3': 'F10'} | {'C2': 'C1', 'C1': 'C2'} | Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
KNeighborsClassifier | C2 | Printer Sales | The model indicates that the label for this case is likely C2, with an 83.33% chance that it is correct, implying that it is unlikely that C1 is the appropriate class. This predictive assertion is chiefly influenced by the values of the input variables F16, F18, and F17. While the F17 and F18 values positively control ... | [
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"neg... | 72 | 249 | {'C2': '83.33%', 'C1': '16.67%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F16, F10, F19 and F2) on the model’s prediction of C2.",
"Summarize the se... | [
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] | {'F17': 'X24', 'F18': 'X1', 'F16': 'X4', 'F10': 'X10', 'F19': 'X2', 'F2': 'X8', 'F1': 'X17', 'F13': 'X7', 'F21': 'X21', 'F26': 'X18', 'F4': 'X6', 'F24': 'X11', 'F8': 'X22', 'F5': 'X25', 'F9': 'X5', 'F12': 'X19', 'F14': 'X15', 'F23': 'X23', 'F3': 'X16', 'F7': 'X3', 'F6': 'X14', 'F20': 'X20', 'F25': 'X13', 'F11': 'X12', ... | {'F24': 'F17', 'F1': 'F18', 'F4': 'F16', 'F10': 'F10', 'F2': 'F19', 'F8': 'F2', 'F17': 'F1', 'F7': 'F13', 'F21': 'F21', 'F18': 'F26', 'F6': 'F4', 'F11': 'F24', 'F22': 'F8', 'F25': 'F5', 'F5': 'F9', 'F19': 'F12', 'F15': 'F14', 'F23': 'F23', 'F16': 'F3', 'F3': 'F7', 'F14': 'F6', 'F20': 'F20', 'F13': 'F25', 'F12': 'F11', ... | {'C2': 'C2', 'C1': 'C1'} | Less | {'C2': 'Less', 'C1': 'More'} |
LogisticRegression | C2 | Hotel Satisfaction | The algorithm's forecast for the data instance under consideration is C2, and the decision's confidence level is about 91.36 percent. We can observe from the plot that the variables F12 and F13 are moving the prediction judgement towards the other label, C1. The F9, F7, F11, and F8, on the other hand, have values that ... | [
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] | 1 | 302 | {'C2': '91.36%', 'C1': '8.64%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F12 (value equal to V0) and F13 (with a value equal to V0).",
"Compare and contrast the impact of the following features (F9, F7, F11 and F8... | [
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] | {'F12': 'Type of Travel', 'F13': 'Type Of Booking', 'F9': 'Hotel wifi service', 'F7': 'Common Room entertainment', 'F11': 'Stay comfort', 'F8': 'Other service', 'F6': 'Checkin\\/Checkout service', 'F14': 'Hotel location', 'F5': 'Food and drink', 'F2': 'Cleanliness', 'F1': 'Age', 'F3': 'Departure\\/Arrival convenience'... | {'F3': 'F12', 'F4': 'F13', 'F6': 'F9', 'F12': 'F7', 'F11': 'F11', 'F14': 'F8', 'F13': 'F6', 'F9': 'F14', 'F10': 'F5', 'F15': 'F2', 'F5': 'F1', 'F7': 'F3', 'F2': 'F4', 'F8': 'F15', 'F1': 'F10'} | {'C2': 'C2', 'C1': 'C1'} | dissatisfied | {'C2': 'dissatisfied', 'C1': 'satisfied'} |
SVC | C1 | Vehicle Insurance Claims | The model classifies this case as C1 and it is noteworthy that there is, however, a 38.26% chance that the true label could be class C2. The uncertainty associated with the classification decision above is higher than expected, which could be attributed to the values of the different input features. The most influentia... | [
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"neg... | 81 | 247 | {'C1': '61.74%', 'C2': '38.26%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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] | {'F1': 'incident_severity', 'F8': 'insured_hobbies', 'F27': 'insured_occupation', 'F15': 'umbrella_limit', 'F9': 'policy_csl', 'F10': 'authorities_contacted', 'F2': 'insured_education_level', 'F32': 'collision_type', 'F19': 'months_as_customer', 'F31': 'vehicle_claim', 'F18': 'insured_relationship', 'F20': 'capital-gai... | {'F27': 'F1', 'F23': 'F8', 'F22': 'F27', 'F5': 'F15', 'F19': 'F9', 'F28': 'F10', 'F21': 'F2', 'F26': 'F32', 'F1': 'F19', 'F16': 'F31', 'F24': 'F18', 'F7': 'F20', 'F33': 'F23', 'F14': 'F13', 'F30': 'F28', 'F20': 'F3', 'F10': 'F25', 'F9': 'F14', 'F2': 'F24', 'F15': 'F17', 'F4': 'F26', 'F32': 'F29', 'F31': 'F7', 'F29': 'F... | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Fraud', 'C2': 'Not Fraud'} |
SVM_linear | C1 | Wine Quality Prediction | The classification or prediction algorithm indicates that the most probable label for the given data is C1 since there is only a 25.47% chance that C2 could be the correct label. The major factors resulting in the above decision are F7, F9, and F1, while the set of features with moderate influence are F6, F3, F10, and ... | [
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] | 296 | 186 | {'C2': '25.47%', 'C1': '74.53%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
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"F6",
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"F10",
"F8",
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] | {'F7': 'sulphates', 'F9': 'volatile acidity', 'F1': 'total sulfur dioxide', 'F6': 'residual sugar', 'F3': 'alcohol', 'F10': 'free sulfur dioxide', 'F8': 'chlorides', 'F5': 'fixed acidity', 'F11': 'citric acid', 'F2': 'pH', 'F4': 'density'} | {'F10': 'F7', 'F2': 'F9', 'F7': 'F1', 'F4': 'F6', 'F11': 'F3', 'F6': 'F10', 'F5': 'F8', 'F1': 'F5', 'F3': 'F11', 'F9': 'F2', 'F8': 'F4'} | {'C2': 'C2', 'C1': 'C1'} | high quality | {'C2': 'low_quality', 'C1': 'high quality'} |
RandomForestClassifier | C1 | Flight Price-Range Classification | The classification verdict is as follows: the most probable label for this case is C1, and the classifier is certain that neither C2 nor C3 is the correct label. The main drivers for the above classification are F12, F7, and F2, all of which have a strong positive influence, pushing the classifier to choose C1. Other ... | [
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"positive",
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"positive",
"positive",
"negative",
"positive",
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] | 250 | 160 | {'C1': '100.00%', 'C2': '0.00%', 'C3': '0.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F12",
"F7",
"F2",
"F1",
"F6",
"F8",
"F10",
"F5",
"F9",
"F3",
"F11",
"F4"
] | {'F12': 'Airline', 'F7': 'Duration_hours', 'F2': 'Total_Stops', 'F1': 'Journey_month', 'F6': 'Source', 'F8': 'Destination', 'F10': 'Arrival_hour', 'F5': 'Journey_day', 'F9': 'Dep_minute', 'F3': 'Arrival_minute', 'F11': 'Duration_mins', 'F4': 'Dep_hour'} | {'F9': 'F12', 'F7': 'F7', 'F12': 'F2', 'F2': 'F1', 'F10': 'F6', 'F11': 'F8', 'F5': 'F10', 'F1': 'F5', 'F4': 'F9', 'F6': 'F3', 'F8': 'F11', 'F3': 'F4'} | {'C1': 'C1', 'C2': 'C2', 'C3': 'C3'} | Low | {'C1': 'Low', 'C2': 'Moderate', 'C3': 'High'} |
RandomForestClassifier | C1 | Ethereum Fraud Detection | The best choice of label for the given case is C1 according to the classification algorithm, since there is little to no chance that C2 is the right class. Not all the features are shown to contribute either positively or negatively towards the label assigned here. The influential features can be ranked according to th... | [
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"neg... | 233 | 139 | {'C2': '0.00%', 'C1': '100.00%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F4, F19, F9, F25 and F31.",
... | [
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"F36",
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"F26",
"F13",
"F34",
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"F2",
... | {'F4': ' ERC20 total Ether sent contract', 'F19': ' ERC20 min val rec', 'F9': 'total transactions (including tnx to create contract', 'F25': ' ERC20 max val rec', 'F31': ' Total ERC20 tnxs', 'F18': ' ERC20 uniq rec addr', 'F33': 'min val sent', 'F37': 'Time Diff between first and last (Mins)', 'F3': 'Sent tnx', 'F11': ... | {'F26': 'F4', 'F31': 'F19', 'F18': 'F9', 'F32': 'F25', 'F23': 'F31', 'F28': 'F18', 'F12': 'F33', 'F3': 'F37', 'F4': 'F3', 'F2': 'F11', 'F9': 'F15', 'F25': 'F27', 'F14': 'F12', 'F13': 'F22', 'F1': 'F1', 'F5': 'F20', 'F37': 'F35', 'F8': 'F5', 'F38': 'F16', 'F30': 'F29', 'F19': 'F17', 'F6': 'F21', 'F36': 'F23', 'F35': 'F6... | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
LogisticRegression | C2 | Student Job Placement | For the given case, the prediction decision is as follows: The probability of C1 being the correct label is only 18.57%, the probability of C2 is 81.43% making it the most probable label for the case here. The certainty of the prediction can be attributed to the influence of variables such as F11, F10, F5, F6, and F2. ... | [
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"positive",
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] | 408 | 195 | {'C1': '18.57%', 'C2': '81.43%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F11",
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"F5",
"F6",
"F2",
"F7",
"F12",
"F4",
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] | {'F11': 'mba_p', 'F10': 'gender', 'F5': 'degree_t', 'F6': 'specialisation', 'F2': 'workex', 'F7': 'hsc_s', 'F12': 'hsc_p', 'F4': 'ssc_p', 'F9': 'ssc_b', 'F3': 'etest_p', 'F1': 'hsc_b', 'F8': 'degree_p'} | {'F5': 'F11', 'F6': 'F10', 'F10': 'F5', 'F12': 'F6', 'F11': 'F2', 'F9': 'F7', 'F2': 'F12', 'F1': 'F4', 'F7': 'F9', 'F4': 'F3', 'F8': 'F1', 'F3': 'F8'} | {'C2': 'C1', 'C1': 'C2'} | Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
SGDClassifier | C1 | Flight Price-Range Classification | The output decision of the classifier with respect to the given case is: C1 is the most probable label, followed by C3 and C2. To be specific, the predicted likelihood across the classes are as follows: 86.54% for C1, 13.46% for C3, and finally a 0.0% probability with respect to C2. The moderately high classification ... | [
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"positive",
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] | 451 | 408 | {'C1': '86.54%', 'C3': '13.46%', 'C2': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F6",
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"F2",
"F1",
"F5",
"F3",
"F4",
"F12",
"F8",
"F10",
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"F11"
] | {'F6': 'Airline', 'F9': 'Total_Stops', 'F2': 'Source', 'F1': 'Journey_month', 'F5': 'Arrival_minute', 'F3': 'Journey_day', 'F4': 'Duration_hours', 'F12': 'Dep_hour', 'F8': 'Destination', 'F10': 'Arrival_hour', 'F7': 'Dep_minute', 'F11': 'Duration_mins'} | {'F9': 'F6', 'F12': 'F9', 'F10': 'F2', 'F2': 'F1', 'F6': 'F5', 'F1': 'F3', 'F7': 'F4', 'F3': 'F12', 'F11': 'F8', 'F5': 'F10', 'F4': 'F7', 'F8': 'F11'} | {'C1': 'C1', 'C2': 'C3', 'C3': 'C2'} | Low | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
LogisticRegression | C1 | Airline Passenger Satisfaction | C1 is the predicted label assigned to this case or instance. This is based on the fact that there is only a 0.68% chance that C2 is the correct label. The most relevant variables that increase the prediction's probability are F11, F5, F9, and F19. Conversely, F2 is the only important feature driving the classification ... | [
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] | 162 | 88 | {'C1': '99.32%', 'C2': '0.68%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
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"F21",
"F7",
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] | {'F11': 'Type of Travel', 'F2': 'Customer Type', 'F5': 'Inflight entertainment', 'F19': 'Inflight wifi service', 'F9': 'Departure\\/Arrival time convenient', 'F12': 'Gate location', 'F18': 'Arrival Delay in Minutes', 'F8': 'Seat comfort', 'F17': 'Online boarding', 'F3': 'Ease of Online booking', 'F16': 'Class', 'F1': '... | {'F4': 'F11', 'F2': 'F2', 'F14': 'F5', 'F7': 'F19', 'F8': 'F9', 'F10': 'F12', 'F22': 'F18', 'F13': 'F8', 'F12': 'F17', 'F9': 'F3', 'F5': 'F16', 'F3': 'F1', 'F15': 'F15', 'F20': 'F14', 'F18': 'F13', 'F19': 'F21', 'F11': 'F7', 'F21': 'F6', 'F17': 'F22', 'F1': 'F20', 'F6': 'F10', 'F16': 'F4'} | {'C2': 'C1', 'C1': 'C2'} | neutral or dissatisfied | {'C1': 'neutral or dissatisfied', 'C2': 'satisfied'} |
RandomForestClassifier | C1 | Personal Loan Modelling | The following classification decisions are largely based on the factors or attributes of this particular case. The class label, in this case, is projected to be C1 out of the potential classes, which is 97.50% likely. The next possible label is C2, which has an approximate probability of 2.50%. The confidence level wi... | [
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"negative",
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] | 454 | 411 | {'C1': '97.50%', 'C2': '2.50%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F4",
"F6",
"F2",
"F1",
"F8",
"F5",
"F9",
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] | {'F4': 'Income', 'F6': 'CD Account', 'F2': 'Education', 'F1': 'Securities Account', 'F8': 'CCAvg', 'F5': 'Family', 'F9': 'Extra_service', 'F7': 'Age', 'F3': 'Mortgage'} | {'F2': 'F4', 'F8': 'F6', 'F5': 'F2', 'F7': 'F1', 'F4': 'F8', 'F3': 'F5', 'F9': 'F9', 'F1': 'F7', 'F6': 'F3'} | {'C1': 'C1', 'C2': 'C2'} | Reject | {'C1': 'Reject', 'C2': 'Accept'} |
DNN | C2 | Ethereum Fraud Detection | The prediction probabilities for classes C1 and C2, respectively, are 15.35% and 84.65%. Based on the aforementioned, C2 is the most likely class label for the presented data instance, and according to the attribution analysis, the various input variables had varying degrees of impact on the model's classification judg... | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F20, F30, F37, F4 and F28.",
... | [
"F20",
"F30",
"F37",
"F4",
"F28",
"F23",
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"F17",
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"F31",
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... | {'F20': ' ERC20 uniq rec contract addr', 'F30': ' ERC20 uniq rec token name', 'F37': 'min value received', 'F4': 'Time Diff between first and last (Mins)', 'F28': 'avg val sent', 'F23': ' ERC20 uniq sent token name', 'F1': 'Sent tnx', 'F36': 'Avg min between received tnx', 'F26': 'Unique Received From Addresses', 'F13'... | {'F30': 'F20', 'F38': 'F30', 'F9': 'F37', 'F3': 'F4', 'F14': 'F28', 'F37': 'F23', 'F4': 'F1', 'F2': 'F36', 'F7': 'F26', 'F28': 'F13', 'F18': 'F16', 'F1': 'F5', 'F29': 'F33', 'F11': 'F38', 'F8': 'F6', 'F10': 'F18', 'F13': 'F27', 'F12': 'F3', 'F6': 'F9', 'F20': 'F24', 'F27': 'F29', 'F24': 'F25', 'F5': 'F2', 'F36': 'F34',... | {'C2': 'C1', 'C1': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
RandomForestClassifier | C2 | Company Bankruptcy Prediction | The model assigns the class C2 with near perfect certainty or confidence level since the predicted likelihood of C1 is only 1.0%. F10, F17, F1, F16, and F36 have the greatest cumulative beneficial influence on the model's choice to create C2. F22 also had a significant influence, but it shifted the choice away from C2.... | [
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"neg... | 54 | 254 | {'C2': '99.00%', 'C1': '1.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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"F39",
"F42",
"F23",
"F43",
"F78",
"F81",
"F67",
"F72",
"F58",
"F55... | {'F10': " Net Income to Stockholder's Equity", 'F1': ' Continuous interest rate (after tax)', 'F16': ' ROA(C) before interest and depreciation before interest', 'F22': ' Borrowing dependency', 'F17': ' Cash Flow Per Share', 'F36': ' Net worth\\/Assets', 'F85': ' Total income\\/Total expense', 'F51': ' Persistent EPS in... | {'F59': 'F10', 'F12': 'F1', 'F29': 'F16', 'F3': 'F22', 'F65': 'F17', 'F84': 'F36', 'F57': 'F85', 'F8': 'F51', 'F10': 'F54', 'F27': 'F29', 'F53': 'F35', 'F42': 'F80', 'F35': 'F65', 'F78': 'F18', 'F31': 'F86', 'F18': 'F53', 'F72': 'F14', 'F23': 'F77', 'F89': 'F6', 'F34': 'F69', 'F87': 'F64', 'F64': 'F37', 'F67': 'F4', 'F... | {'C1': 'C2', 'C2': 'C1'} | No | {'C2': 'No', 'C1': 'Yes'} |
RandomForestClassifier | C2 | House Price Classification | Between the two classes, the model labelled this case as C2 with a likelihood of about 97.0% since there is only a marginal chance that it belongs to label C1. The most relevant features influencing this decision are F10, F13, F11, and F2. In this case, F10, F13, and F2 have a considerable positive influence on the pre... | [
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"0.01",
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"-0.01",
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] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive"
] | 125 | 58 | {'C1': '3.00%', 'C2': '97.00%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the predic... | [
"F10",
"F13",
"F2",
"F11",
"F7",
"F3",
"F6",
"F4",
"F5",
"F8",
"F12",
"F1",
"F9"
] | {'F10': 'LSTAT', 'F13': 'RM', 'F2': 'AGE', 'F11': 'TAX', 'F7': 'PTRATIO', 'F3': 'DIS', 'F6': 'CRIM', 'F4': 'RAD', 'F5': 'B', 'F8': 'NOX', 'F12': 'ZN', 'F1': 'INDUS', 'F9': 'CHAS'} | {'F13': 'F10', 'F6': 'F13', 'F7': 'F2', 'F10': 'F11', 'F11': 'F7', 'F8': 'F3', 'F1': 'F6', 'F9': 'F4', 'F12': 'F5', 'F5': 'F8', 'F2': 'F12', 'F3': 'F1', 'F4': 'F9'} | {'C2': 'C1', 'C1': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
LogisticRegression | C1 | Concrete Strength Classification | Probably C1 is the right label for this case since the probability of the alternative label, C2 and C3, are only 1.03% and 0.0%. The order of importance of the features for the above classification verdict is F6, F2, F8, F4, F5, F3, F1, and F7. Analysis conducted shows that only the features F2, F5, and F3 have negativ... | [
"0.40",
"-0.24",
"0.14",
"0.12",
"-0.10",
"-0.08",
"0.02",
"0.00"
] | [
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive"
] | 178 | 207 | {'C2': '1.03%', 'C1': '98.97%', 'C3': '0.0%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F6, F2 and F8.",
"Summarize ... | [
"F6",
"F2",
"F8",
"F4",
"F5",
"F3",
"F1",
"F7"
] | {'F6': 'cement', 'F2': 'age_days', 'F8': 'water', 'F4': 'superplasticizer', 'F5': 'fineaggregate', 'F3': 'flyash', 'F1': 'slag', 'F7': 'coarseaggregate'} | {'F1': 'F6', 'F8': 'F2', 'F4': 'F8', 'F5': 'F4', 'F7': 'F5', 'F3': 'F3', 'F2': 'F1', 'F6': 'F7'} | {'C3': 'C2', 'C1': 'C1', 'C2': 'C3'} | Strong | {'C2': 'Weak', 'C1': 'Strong', 'C3': 'Other'} |
DNN | C2 | Credit Card Fraud Classification | The model labels the given data as C2 since it has a higher predicted probability equal to 51.42% compared to that of C1 which is equal to 48.58%. The input variables with higher contributions to the above classification decision are F22, F14, F2, F19, and F9, while those with little influence are F8, F13, F30, F1, and... | [
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"positiv... | 241 | 147 | {'C1': '48.58%', 'C2': '51.42%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F14",
"F22",
"F2",
"F19",
"F9",
"F20",
"F5",
"F3",
"F18",
"F17",
"F4",
"F10",
"F29",
"F6",
"F28",
"F16",
"F15",
"F11",
"F12",
"F24",
"F26",
"F25",
"F7",
"F27",
"F21",
"F8",
"F13",
"F30",
"F1",
"F23"
] | {'F14': 'Z18', 'F22': 'Z14', 'F2': 'Time', 'F19': 'Z1', 'F9': 'Z19', 'F20': 'Z10', 'F5': 'Z4', 'F3': 'Z3', 'F18': 'Z12', 'F17': 'Z16', 'F4': 'Z7', 'F10': 'Z11', 'F29': 'Z9', 'F6': 'Z6', 'F28': 'Z23', 'F16': 'Z5', 'F15': 'Z17', 'F11': 'Z21', 'F12': 'Z24', 'F24': 'Z8', 'F26': 'Amount', 'F25': 'Z20', 'F7': 'Z27', 'F27': '... | {'F19': 'F14', 'F15': 'F22', 'F1': 'F2', 'F2': 'F19', 'F20': 'F9', 'F11': 'F20', 'F5': 'F5', 'F4': 'F3', 'F13': 'F18', 'F17': 'F17', 'F8': 'F4', 'F12': 'F10', 'F10': 'F29', 'F7': 'F6', 'F24': 'F28', 'F6': 'F16', 'F18': 'F15', 'F22': 'F11', 'F25': 'F12', 'F9': 'F24', 'F30': 'F26', 'F21': 'F25', 'F28': 'F7', 'F26': 'F27'... | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
SGDClassifier | C2 | Company Bankruptcy Prediction | The following is the classification for the provided data: C2 is the most likely class label and C1 cannot possibly be the correct label given the likelihood is 0.0%. F73, F34, and F8 are the key variables that contributed to the classification choice. However, the classifier does not consider all features while makin... | [
"-0.30",
"-0.11",
"0.10",
"-0.10",
"-0.09",
"0.07",
"-0.06",
"-0.05",
"0.05",
"0.05",
"-0.05",
"0.04",
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"-0.03",
"0.03",
"0.03",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"... | [
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negligible",
"negligible",
"neg... | 257 | 336 | {'C1': '0.00%', 'C2': '100.00%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F73 and F34.",
"Summarize th... | [
"F73",
"F34",
"F8",
"F36",
"F46",
"F32",
"F20",
"F51",
"F5",
"F27",
"F12",
"F19",
"F83",
"F1",
"F68",
"F89",
"F15",
"F6",
"F61",
"F81",
"F43",
"F39",
"F10",
"F91",
"F33",
"F55",
"F41",
"F70",
"F30",
"F93",
"F84",
"F22",
"F66",
"F74",
"F82",
"F2",... | {'F73': ' Liability to Equity', 'F34': ' Net worth\\/Assets', 'F8': ' Debt ratio %', 'F36': " Net Income to Stockholder's Equity", 'F46': ' Equity to Liability', 'F32': ' Realized Sales Gross Margin', 'F20': ' Net Value Per Share (A)', 'F51': ' Current Liability to Assets', 'F5': ' Current Liability to Equity', 'F27': ... | {'F66': 'F73', 'F84': 'F34', 'F47': 'F8', 'F59': 'F36', 'F91': 'F46', 'F83': 'F32', 'F42': 'F20', 'F46': 'F51', 'F92': 'F5', 'F16': 'F27', 'F63': 'F12', 'F13': 'F19', 'F67': 'F83', 'F8': 'F1', 'F39': 'F68', 'F44': 'F89', 'F88': 'F15', 'F93': 'F6', 'F48': 'F61', 'F32': 'F81', 'F9': 'F43', 'F73': 'F39', 'F72': 'F10', 'F7... | {'C2': 'C1', 'C1': 'C2'} | Yes | {'C1': 'No', 'C2': 'Yes'} |
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