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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SVM_linear | C1 | Wine Quality Prediction | The classifier assigns the label C1 since the probability associated with C1 is greater than that of C2. For the case under consideration, F5, F3, F10, and F7 are the sets of features significantly influencing the decision made by the classifier. However, features such as F4, F11, and F8 have limited to no impact on th... | [
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"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 175 | 99 | {'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 (F5, F3, F7 and F10) on the prediction made for this test case.",
"Compare the direction of impact of the features: F2, F1 and F6.",
"Describe the degree of... | [
"F5",
"F3",
"F7",
"F10",
"F2",
"F1",
"F6",
"F9",
"F4",
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] | {'F5': 'residual sugar', 'F3': 'volatile acidity', 'F7': 'alcohol', 'F10': 'fixed acidity', 'F2': 'chlorides', 'F1': 'sulphates', 'F6': 'citric acid', 'F9': 'free sulfur dioxide', 'F4': 'density', 'F11': 'total sulfur dioxide', 'F8': 'pH'} | {'F4': 'F5', 'F2': 'F3', 'F11': 'F7', 'F1': 'F10', 'F5': 'F2', 'F10': 'F1', 'F3': 'F6', 'F6': 'F9', 'F8': 'F4', 'F7': 'F11', 'F9': 'F8'} | {'C1': 'C2', 'C2': 'C1'} | high quality | {'C2': 'low_quality', 'C1': 'high quality'} |
KNeighborsClassifier | C1 | Cab Surge Pricing System | The probability that C3 is the label for the given case is zero and judging by the predicted probability associated with the remaining classes, the classifier is fairly certain that the correct label is C1 given its likelihood of 75.0%. The features are ranked in order of their respective impacts, from most important t... | [
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"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
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] | 180 | 281 | {'C2': '25.00%', 'C1': '75.00%', 'C3': '0.00%'} | [
"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: F5 and F3.",
"Summarize the ... | [
"F5",
"F3",
"F11",
"F9",
"F10",
"F4",
"F2",
"F7",
"F12",
"F6",
"F1",
"F8"
] | {'F5': 'Type_of_Cab', 'F3': 'Confidence_Life_Style_Index', 'F11': 'Trip_Distance', 'F9': 'Cancellation_Last_1Month', 'F10': 'Life_Style_Index', 'F4': 'Customer_Since_Months', 'F2': 'Customer_Rating', 'F7': 'Var2', 'F12': 'Destination_Type', 'F6': 'Gender', 'F1': 'Var1', 'F8': 'Var3'} | {'F2': 'F5', 'F5': 'F3', 'F1': 'F11', 'F8': 'F9', 'F4': 'F10', 'F3': 'F4', 'F7': 'F2', 'F10': 'F7', 'F6': 'F12', 'F12': 'F6', 'F9': 'F1', 'F11': 'F8'} | {'C1': 'C2', 'C3': 'C1', 'C2': 'C3'} | C2 | {'C2': 'Low', 'C1': 'Medium', 'C3': 'High'} |
BernoulliNB | C1 | Personal Loan Modelling | The most likely label for the given example based on the values of the variables is C1, according to the prediction probability of each class label. It can be concluded that the classifier is quite certain that C1 is the correct label because the probability of C2 is small. According to the attributions of the input va... | [
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] | [
"positive",
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"positive",
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"negative",
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] | 245 | 313 | {'C1': '99.99%', 'C2': '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... | [
"F4",
"F7",
"F2",
"F3",
"F5",
"F8",
"F6",
"F9",
"F1"
] | {'F4': 'CD Account', 'F7': 'Income', 'F2': 'CCAvg', 'F3': 'Securities Account', 'F5': 'Education', 'F8': 'Family', 'F6': 'Mortgage', 'F9': 'Age', 'F1': 'Extra_service'} | {'F8': 'F4', 'F2': 'F7', 'F4': 'F2', 'F7': 'F3', 'F5': 'F5', 'F3': 'F8', 'F6': 'F6', 'F1': 'F9', 'F9': 'F1'} | {'C1': 'C1', 'C2': 'C2'} | Reject | {'C1': 'Reject', 'C2': 'Accept'} |
BernoulliNB | C1 | Hotel Satisfaction | The given case is labelled as C1 since it has a prediction probability of 98.33% which implies that C2 is the least probable label. The higher confidence in the assigned label is mainly due to the contributions of input features F7, F11, and F8. In contrast, F2, F4, and F6 are the least ranked features. Based on featur... | [
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] | 275 | 182 | {'C2': '1.67%', 'C1': '98.33%'} | [
"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... | [
"F7",
"F11",
"F8",
"F13",
"F14",
"F9",
"F1",
"F15",
"F10",
"F5",
"F12",
"F3",
"F2",
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"F6"
] | {'F7': 'Type Of Booking', 'F11': 'Type of Travel', 'F8': 'Common Room entertainment', 'F13': 'Stay comfort', 'F14': 'Hotel wifi service', 'F9': 'Checkin\\/Checkout service', 'F1': 'Cleanliness', 'F15': 'Other service', 'F10': 'Age', 'F5': 'Food and drink', 'F12': 'Ease of Online booking', 'F3': 'Departure\\/Arrival co... | {'F4': 'F7', 'F3': 'F11', 'F12': 'F8', 'F11': 'F13', 'F6': 'F14', 'F13': 'F9', 'F15': 'F1', 'F14': 'F15', 'F5': 'F10', 'F10': 'F5', 'F8': 'F12', 'F7': 'F3', 'F9': 'F2', 'F1': 'F4', 'F2': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | satisfied | {'C2': 'dissatisfied', 'C1': 'satisfied'} |
KNeighborsClassifier | C2 | Cab Surge Pricing System | The correct label, according to the classifier, is neither C3 nor C1, but C2, with a prediction likelihood of about 75.0%. By analysing the attributions of the input features, they can be ranked according to the level of impact, from the most important feature to the least relevant, as follows: F6, F5, F9, F2, F12, F7... | [
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"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 180 | 283 | {'C3': '25.00%', 'C2': '75.00%', 'C1': '0.00%'} | [
"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: F6 and F5.",
"Summarize the ... | [
"F6",
"F5",
"F9",
"F2",
"F12",
"F7",
"F3",
"F4",
"F10",
"F1",
"F11",
"F8"
] | {'F6': 'Type_of_Cab', 'F5': 'Confidence_Life_Style_Index', 'F9': 'Trip_Distance', 'F2': 'Cancellation_Last_1Month', 'F12': 'Life_Style_Index', 'F7': 'Customer_Since_Months', 'F3': 'Customer_Rating', 'F4': 'Var2', 'F10': 'Destination_Type', 'F1': 'Gender', 'F11': 'Var1', 'F8': 'Var3'} | {'F2': 'F6', 'F5': 'F5', 'F1': 'F9', 'F8': 'F2', 'F4': 'F12', 'F3': 'F7', 'F7': 'F3', 'F10': 'F4', 'F6': 'F10', 'F12': 'F1', 'F9': 'F11', 'F11': 'F8'} | {'C3': 'C3', 'C1': 'C2', 'C2': 'C1'} | C2 | {'C3': 'Low', 'C2': 'Medium', 'C1': 'High'} |
KNeighborsClassifier | C1 | Basketball Players Career Length Prediction | It is important to note that the classifier's labelling decision is based solely on the information supplied. The classification verdict is as follows: C1 is the most probable label with respect to the case under consideration, since the prediction likelihood of the other label, C2, is only 12.50%. The most important v... | [
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"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
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] | 14 | 370 | {'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, F18 and F6) on the prediction made for this test case.",
"Compare the direction of impact of the features: F8, F9 and F14.",
"Describe the degree of im... | [
"F1",
"F18",
"F6",
"F8",
"F9",
"F14",
"F13",
"F3",
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"F15",
"F16",
"F11",
"F12",
"F19",
"F4",
"F7",
"F2",
"F17",
"F5"
] | {'F1': 'GamesPlayed', 'F18': 'OffensiveRebounds', 'F6': 'FieldGoalPercent', 'F8': 'FreeThrowMade', 'F9': 'FreeThrowPercent', 'F14': 'Rebounds', 'F13': 'FreeThrowAttempt', 'F3': 'FieldGoalsMade', 'F10': 'PointsPerGame', 'F15': '3PointAttempt', 'F16': 'DefensiveRebounds', 'F11': 'MinutesPlayed', 'F12': 'Blocks', 'F19': '... | {'F1': 'F1', 'F13': 'F18', 'F6': 'F6', 'F10': 'F8', 'F12': 'F9', 'F15': 'F14', 'F11': 'F13', 'F4': 'F3', 'F3': 'F10', 'F8': 'F15', 'F14': 'F16', 'F2': 'F11', 'F18': 'F12', 'F19': 'F19', 'F9': 'F4', 'F16': 'F7', 'F5': 'F2', 'F7': 'F17', 'F17': 'F5'} | {'C2': 'C1', 'C1': 'C2'} | More than 5 | {'C1': 'More than 5', 'C2': 'Less than 5'} |
DecisionTreeClassifier | C2 | Concrete Strength Classification | According to the classification algorithm or model, C2 is the most likely class, with a very high confidence level, and C1 has a very low likelihood of being the right label. All of the inputs are proven to contribute to the categorization described above and the following is a ordering of the features from least essen... | [
"0.32",
"0.18",
"0.17",
"0.07",
"0.05",
"0.05",
"0.04",
"0.03"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive"
] | 246 | 348 | {'C2': '100.00%', '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... | [
"F3",
"F8",
"F7",
"F1",
"F2",
"F6",
"F5",
"F4"
] | {'F3': 'age_days', 'F8': 'superplasticizer', 'F7': 'cement', 'F1': 'coarseaggregate', 'F2': 'fineaggregate', 'F6': 'water', 'F5': 'slag', 'F4': 'flyash'} | {'F8': 'F3', 'F5': 'F8', 'F1': 'F7', 'F6': 'F1', 'F7': 'F2', 'F4': 'F6', 'F2': 'F5', 'F3': 'F4'} | {'C1': 'C2', 'C2': 'C1'} | Weak | {'C2': 'Weak', 'C1': 'Strong'} |
RandomForestClassifier | C3 | Mobile Price-Range Classification | The model indicates that C4 and C1 have zero prediction probabilities, while that of C2 is 3.85%, meaning the most probable label for the given case is C3 and the confidence level is approximately equal to 96.15% certainty. The major features driving the above classification are F10, F14, and F7, while the least relev... | [
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] | [
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"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
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"negative"
] | 247 | 157 | {'C4': '0.00%', 'C1': '0.00%', 'C2': '3.85%', 'C3': '96.15%'} | [
"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... | [
"F14",
"F10",
"F7",
"F11",
"F13",
"F1",
"F18",
"F2",
"F6",
"F20",
"F12",
"F5",
"F15",
"F8",
"F9",
"F16",
"F4",
"F17",
"F3",
"F19"
] | {'F14': 'ram', 'F10': 'battery_power', 'F7': 'px_width', 'F11': 'int_memory', 'F13': 'pc', 'F1': 'touch_screen', 'F18': 'four_g', 'F2': 'm_dep', 'F6': 'px_height', 'F20': 'clock_speed', 'F12': 'sc_h', 'F5': 'n_cores', 'F15': 'talk_time', 'F8': 'blue', 'F9': 'dual_sim', 'F16': 'fc', 'F4': 'mobile_wt', 'F17': 'sc_w', 'F3... | {'F11': 'F14', 'F1': 'F10', 'F10': 'F7', 'F4': 'F11', 'F8': 'F13', 'F19': 'F1', 'F17': 'F18', 'F5': 'F2', 'F9': 'F6', 'F2': 'F20', 'F12': 'F12', 'F7': 'F5', 'F14': 'F15', 'F15': 'F8', 'F16': 'F9', 'F3': 'F16', 'F6': 'F4', 'F13': 'F17', 'F20': 'F3', 'F18': 'F19'} | {'C1': 'C4', 'C4': 'C1', 'C2': 'C2', 'C3': 'C3'} | r4 | {'C4': 'r1', 'C1': 'r2', 'C2': 'r3', 'C3': 'r4'} |
SVM_poly | C4 | Mobile Price-Range Classification | The predicted output label from the model is C4 with almost 100% certainty, indicating it is very certain it is correct and this is mainly because the likelihoods across the other labels C3, C1, and C2 are 0.47%, 0.05%, and 0.04%, respectively. Among the top features F11, F12, and F5, the features F5 and F12 positively... | [
"0.78",
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] | [
"positive",
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"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative"
] | 47 | 15 | {'C4': '99.45%', 'C3': '0.47%', 'C2': '0.04%', 'C1': '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: F5, F12 and F11.",
"Compare and contrast the impact of the following features (F14, F2 (value equal to V1) and F20 (value equal to V1)) on t... | [
"F5",
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"F11",
"F14",
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"F20",
"F17",
"F4",
"F15",
"F9",
"F19",
"F8",
"F6",
"F18",
"F1",
"F3",
"F10",
"F7",
"F13",
"F16"
] | {'F5': 'ram', 'F12': 'battery_power', 'F11': 'px_height', 'F14': 'px_width', 'F2': 'dual_sim', 'F20': 'four_g', 'F17': 'touch_screen', 'F4': 'int_memory', 'F15': 'pc', 'F9': 'n_cores', 'F19': 'fc', 'F8': 'clock_speed', 'F6': 'three_g', 'F18': 'sc_w', 'F1': 'wifi', 'F3': 'm_dep', 'F10': 'mobile_wt', 'F7': 'talk_time', '... | {'F11': 'F5', 'F1': 'F12', 'F9': 'F11', 'F10': 'F14', 'F16': 'F2', 'F17': 'F20', 'F19': 'F17', 'F4': 'F4', 'F8': 'F15', 'F7': 'F9', 'F3': 'F19', 'F2': 'F8', 'F18': 'F6', 'F13': 'F18', 'F20': 'F1', 'F5': 'F3', 'F6': 'F10', 'F14': 'F7', 'F12': 'F13', 'F15': 'F16'} | {'C2': 'C4', 'C3': 'C3', 'C1': 'C2', 'C4': 'C1'} | r1 | {'C4': 'r1', 'C3': 'r2', 'C2': 'r3', 'C1': 'r4'} |
SVC | C2 | Paris House Classification | The prediction probabilities associated with the classes C2 and C1 are 99.56% and 0.44%, respectively. Therefore, we can conclude that the most probable label for the given data is C2. The classification model's decision here is largely based on the impacts of the F6, F8, and F15, whereas the F17, F4, and F14 have very... | [
"0.35",
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] | [
"positive",
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"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 221 | 448 | {'C2': '99.56%', 'C1': '0.44%'} | [
"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",
"F8",
"F15",
"F7",
"F16",
"F10",
"F11",
"F13",
"F2",
"F9",
"F3",
"F1",
"F12",
"F5",
"F4",
"F17",
"F14"
] | {'F6': 'isNewBuilt', 'F8': 'hasYard', 'F15': 'hasPool', 'F7': 'hasStormProtector', 'F16': 'hasStorageRoom', 'F10': 'made', 'F11': 'numberOfRooms', 'F13': 'basement', 'F2': 'squareMeters', 'F9': 'numPrevOwners', 'F3': 'floors', 'F1': 'garage', 'F12': 'attic', 'F5': 'price', 'F4': 'cityCode', 'F17': 'cityPartRange', 'F14... | {'F3': 'F6', 'F1': 'F8', 'F2': 'F15', 'F4': 'F7', 'F5': 'F16', 'F12': 'F10', 'F7': 'F11', 'F13': 'F13', 'F6': 'F2', 'F11': 'F9', 'F8': 'F3', 'F15': 'F1', 'F14': 'F12', 'F17': 'F5', 'F9': 'F4', 'F10': 'F17', 'F16': 'F14'} | {'C1': 'C2', 'C2': 'C1'} | Basic | {'C2': 'Basic', 'C1': 'Luxury'} |
GradientBoostingClassifier | C1 | Basketball Players Career Length Prediction | The model identifies the case as C1 since, the true label has just 33.63 percent chance of being C2 when the prediction probability is calculated. The in-depth analysis found that the bulk of the attributes had negative impacts, driving the prediction away from C1 and toward C2. F7, F14, F4, F19, and F3 are among the f... | [
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] | [
"negative",
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"positive",
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"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative"
] | 150 | 274 | {'C2': '33.63%', 'C1': '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: F7, F14 and F4.",
"Summarize... | [
"F7",
"F14",
"F4",
"F19",
"F3",
"F2",
"F17",
"F10",
"F13",
"F12",
"F5",
"F8",
"F9",
"F15",
"F11",
"F18",
"F16",
"F1",
"F6"
] | {'F7': 'GamesPlayed', 'F14': 'OffensiveRebounds', 'F4': 'FieldGoalPercent', 'F19': 'FreeThrowPercent', 'F3': '3PointPercent', 'F2': '3PointAttempt', 'F17': 'FieldGoalsMade', 'F10': 'Blocks', 'F13': 'DefensiveRebounds', 'F12': 'Turnovers', 'F5': 'Rebounds', 'F8': 'FreeThrowAttempt', 'F9': 'MinutesPlayed', 'F15': 'Assist... | {'F1': 'F7', 'F13': 'F14', 'F6': 'F4', 'F12': 'F19', 'F9': 'F3', 'F8': 'F2', 'F4': 'F17', 'F18': 'F10', 'F14': 'F13', 'F19': 'F12', 'F15': 'F5', 'F11': 'F8', 'F2': 'F9', 'F16': 'F15', 'F5': 'F11', 'F7': 'F18', 'F3': 'F16', 'F10': 'F1', 'F17': 'F6'} | {'C2': 'C2', 'C1': 'C1'} | Less than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
KNeighborsClassifier | C1 | Cab Surge Pricing System | 0.0% is the predicted probability that C2 is the true label for the test example under consideration according to the classifier. Judging based on the predicted probabilities associated with the other remaining labels, the classifier is 75.0% confident that C1 is the correct label. From the analysis, the features rank... | [
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"positive",
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"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
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"positive"
] | 180 | 104 | {'C3': '25.00%', 'C1': '75.00%', 'C2': '0.00%'} | [
"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: F5 and F4.",
"Summarize the ... | [
"F5",
"F4",
"F10",
"F11",
"F7",
"F8",
"F9",
"F6",
"F3",
"F2",
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"F12"
] | {'F5': 'Type_of_Cab', 'F4': 'Confidence_Life_Style_Index', 'F10': 'Trip_Distance', 'F11': 'Cancellation_Last_1Month', 'F7': 'Life_Style_Index', 'F8': 'Customer_Since_Months', 'F9': 'Customer_Rating', 'F6': 'Var2', 'F3': 'Destination_Type', 'F2': 'Gender', 'F1': 'Var1', 'F12': 'Var3'} | {'F2': 'F5', 'F5': 'F4', 'F1': 'F10', 'F8': 'F11', 'F4': 'F7', 'F3': 'F8', 'F7': 'F9', 'F10': 'F6', 'F6': 'F3', 'F12': 'F2', 'F9': 'F1', 'F11': 'F12'} | {'C1': 'C3', 'C3': 'C1', 'C2': 'C2'} | C2 | {'C3': 'Low', 'C1': 'Medium', 'C2': 'High'} |
SGDClassifier | C2 | House Price Classification | The classifier is very certain that C1 is not the true label since the predicted probability of C2 is given as 100.0%. Analysing the attributions of the features indicates that the most relevant features are F8, F13, F3, and F2 while F6, F11, and F12 are the least relevant features. The values of F7, F5, F10, F1, F4, ... | [
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"negative",
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"positive",
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"positive",
"positive",
"positive",
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] | 446 | 403 | {'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... | [
"F13",
"F8",
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"F2",
"F7",
"F5",
"F10",
"F1",
"F4",
"F9",
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"F12"
] | {'F13': 'CRIM', 'F8': 'LSTAT', 'F3': 'RAD', 'F2': 'AGE', 'F7': 'CHAS', 'F5': 'DIS', 'F10': 'ZN', 'F1': 'TAX', 'F4': 'PTRATIO', 'F9': 'B', 'F6': 'RM', 'F11': 'NOX', 'F12': 'INDUS'} | {'F1': 'F13', 'F13': 'F8', 'F9': 'F3', 'F7': 'F2', 'F4': 'F7', 'F8': 'F5', 'F2': 'F10', 'F10': 'F1', 'F11': 'F4', 'F12': 'F9', 'F6': 'F6', 'F5': 'F11', 'F3': 'F12'} | {'C2': 'C2', 'C1': 'C1'} | Low | {'C2': 'Low', 'C1': 'High'} |
GradientBoostingClassifier | C1 | Health Care Services Satisfaction Prediction | Based on the information provided to the classifier, the true label for the given case is likely C1, with a confidence level of 76.26%. Each input variable has a different degree of influence on the classifier's final labelling decision with respect to the case under consideration. Whilst F8, F12, and F9 have lower con... | [
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"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive"
] | 35 | 390 | {'C2': '23.74%', 'C1': '76.26%'} | [
"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 (value equal to V3), F13 (with a value equal to V3) and F3 (equal to ... | [
"F7",
"F2",
"F16",
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"F13",
"F3",
"F1",
"F6",
"F10",
"F15",
"F4",
"F8",
"F12",
"F9"
] | {'F7': 'Exact diagnosis', 'F2': 'avaliablity of drugs', 'F16': 'lab services', 'F11': 'friendly health care workers', 'F5': 'Communication with dr', 'F14': 'Time waiting', 'F13': 'Specialists avaliable', 'F3': 'Modern equipment', 'F1': 'waiting rooms', 'F6': 'Check up appointment', 'F10': 'Hygiene and cleaning', 'F15':... | {'F9': 'F7', 'F13': 'F2', 'F12': 'F16', 'F11': 'F11', 'F8': 'F5', 'F2': 'F14', 'F7': 'F13', 'F10': 'F3', 'F14': 'F1', 'F1': 'F6', 'F4': 'F10', 'F3': 'F15', 'F5': 'F4', 'F15': 'F8', 'F16': 'F12', 'F6': 'F9'} | {'C2': 'C2', 'C1': 'C1'} | Satisfied | {'C2': 'Dissatisfied', 'C1': 'Satisfied'} |
SVC | C2 | Health Care Services Satisfaction Prediction | The classification model employed made its label selection decision based on the information provided about the case under consideration. With a moderately low degree of confidence, it classifies the case under consideration as C2. Specifically, per the model, the probability of labelling the case as C1 is equal to 48.... | [
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"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 449 | 406 | {'C1': '48.66%', 'C2': '51.34%'} | [
"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",
"F4",
"F11",
"F16",
"F15",
"F6",
"F5",
"F13",
"F9",
"F1",
"F2",
"F12",
"F3",
"F8",
"F7",
"F10"
] | {'F14': 'lab services', 'F4': 'Specialists avaliable', 'F11': 'Quality\\/experience dr.', 'F16': 'Exact diagnosis', 'F15': 'Hygiene and cleaning', 'F6': 'avaliablity of drugs', 'F5': 'Time waiting', 'F13': 'Check up appointment', 'F9': 'hospital rooms quality', 'F1': 'Modern equipment', 'F2': 'Time of appointment', 'F1... | {'F12': 'F14', 'F7': 'F4', 'F6': 'F11', 'F9': 'F16', 'F4': 'F15', 'F13': 'F6', 'F2': 'F5', 'F1': 'F13', 'F15': 'F9', 'F10': 'F1', 'F5': 'F2', 'F11': 'F12', 'F8': 'F3', 'F14': 'F8', 'F16': 'F7', 'F3': 'F10'} | {'C2': 'C1', 'C1': 'C2'} | Satisfied | {'C1': 'Dissatisfied', 'C2': 'Satisfied'} |
SVC | C2 | Flight Price-Range Classification | The prediction results are as follows: the probability that C2 is the correct label is 97.12%, the probability that C3 is the correct label is 2.55%, and the probability that C1 is the correct label is 0.33%. Judging based on the prediction probabilities across the classes, C2 is the most probable label. The very high... | [
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"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
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] | 409 | 196 | {'C2': '97.12%', 'C3': '2.55%', 'C1': '0.33%'} | [
"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... | [
"F10",
"F9",
"F8",
"F5",
"F12",
"F11",
"F2",
"F1",
"F7",
"F3",
"F6",
"F4"
] | {'F10': 'Total_Stops', 'F9': 'Airline', 'F8': 'Duration_hours', 'F5': 'Journey_month', 'F12': 'Source', 'F11': 'Journey_day', 'F2': 'Arrival_hour', 'F1': 'Duration_mins', 'F7': 'Arrival_minute', 'F3': 'Dep_hour', 'F6': 'Destination', 'F4': 'Dep_minute'} | {'F12': 'F10', 'F9': 'F9', 'F7': 'F8', 'F2': 'F5', 'F10': 'F12', 'F1': 'F11', 'F5': 'F2', 'F8': 'F1', 'F6': 'F7', 'F3': 'F3', 'F11': 'F6', 'F4': 'F4'} | {'C1': 'C2', 'C2': 'C3', 'C3': 'C1'} | Low | {'C2': 'Low', 'C3': 'Moderate', 'C1': 'High'} |
DecisionTreeClassifier | C2 | Insurance Churn | C2 is the model's predicted output for this given case, with an accuracy of 87.13% meaning the likelihood of C1 is only 12.87%. F14, F6, F16, F8, and F1 have the most effect on the output prediction choice in this case, whereas on the other hand, F7, F11, F4, and F13 are not that important to the decision made here. F1... | [
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"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative"
] | 107 | 351 | {'C2': '87.13%', 'C1': '12.87%'} | [
"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... | [
"F14",
"F6",
"F1",
"F16",
"F8",
"F9",
"F15",
"F10",
"F12",
"F2",
"F5",
"F3",
"F7",
"F11",
"F4",
"F13"
] | {'F14': 'feature3', 'F6': 'feature15', 'F1': 'feature11', 'F16': 'feature12', 'F8': 'feature13', 'F9': 'feature14', 'F15': 'feature5', 'F10': 'feature0', 'F12': 'feature7', 'F2': 'feature10', 'F5': 'feature6', 'F3': 'feature4', 'F7': 'feature9', 'F11': 'feature2', 'F4': 'feature8', 'F13': 'feature1'} | {'F13': 'F14', 'F9': 'F6', 'F5': 'F1', 'F6': 'F16', 'F7': 'F8', 'F8': 'F9', 'F15': 'F15', 'F10': 'F10', 'F1': 'F12', 'F4': 'F2', 'F16': 'F5', 'F14': 'F3', 'F3': 'F7', 'F12': 'F11', 'F2': 'F4', 'F11': 'F13'} | {'C1': 'C2', 'C2': 'C1'} | Stay | {'C2': 'Stay', 'C1': 'Leave'} |
SVM_linear | C2 | Employee Promotion Prediction | The classification model or algorithm classifies the provided data or case as C2 with a predicted likelihood of 94.16%, meaning that the chance of C1 being the true label is only 5.84%. The most relevant features driving the classification above are F11, F9, F10, F3, and F1, however, arranging the input features in-ord... | [
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] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
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"positive",
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] | 26 | 379 | {'C1': '5.84%', 'C2': '94.16%'} | [
"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... | [
"F11",
"F9",
"F10",
"F3",
"F1",
"F5",
"F4",
"F2",
"F6",
"F7",
"F8"
] | {'F11': 'department', 'F9': 'avg_training_score', 'F10': 'KPIs_met >80%', 'F3': 'recruitment_channel', 'F1': 'region', 'F5': 'education', 'F4': 'length_of_service', 'F2': 'age', 'F6': 'no_of_trainings', 'F7': 'gender', 'F8': 'previous_year_rating'} | {'F1': 'F11', 'F11': 'F9', 'F10': 'F10', 'F5': 'F3', 'F2': 'F1', 'F3': 'F5', 'F9': 'F4', 'F7': 'F2', 'F6': 'F6', 'F4': 'F7', 'F8': 'F8'} | {'C2': 'C1', 'C1': 'C2'} | Promote | {'C1': 'Ignore', 'C2': 'Promote'} |
GradientBoostingClassifier | C1 | Basketball Players Career Length Prediction | The final classification made was C1, but with a likelihood of only 55.19%, the model is uncertain about this prediction. By far, feature F16 had the most impact and following F16 are F3, F15, and F6 have been identified as having the comparable influence on classification. The combination of F16, F3, F15, F6, and F7 f... | [
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"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 88 | 36 | {'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: F16, F3, F15, F6 and F7.",
"... | [
"F16",
"F3",
"F15",
"F6",
"F7",
"F1",
"F12",
"F19",
"F8",
"F13",
"F11",
"F9",
"F18",
"F2",
"F4",
"F5",
"F14",
"F17",
"F10"
] | {'F16': 'GamesPlayed', 'F3': 'OffensiveRebounds', 'F15': 'FieldGoalPercent', 'F6': 'FreeThrowPercent', 'F7': '3PointPercent', 'F1': '3PointAttempt', 'F12': 'FieldGoalsMade', 'F19': 'Blocks', 'F8': 'DefensiveRebounds', 'F13': 'Turnovers', 'F11': 'Rebounds', 'F9': 'MinutesPlayed', 'F18': 'FreeThrowAttempt', 'F2': '3Point... | {'F1': 'F16', 'F13': 'F3', 'F6': 'F15', 'F12': 'F6', 'F9': 'F7', 'F8': 'F1', 'F4': 'F12', 'F18': 'F19', 'F14': 'F8', 'F19': 'F13', 'F15': 'F11', 'F2': 'F9', 'F11': 'F18', 'F7': 'F2', 'F16': 'F4', 'F3': 'F5', 'F10': 'F14', 'F5': 'F17', 'F17': 'F10'} | {'C1': 'C2', 'C2': 'C1'} | Less than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
BernoulliNB | C2 | Personal Loan Modelling | Based on the prediction probabilities, C2 is the most likely label for the given case considering the values of the input variables and because the likelihood of C1 is very marginal, so the classifier is very confident that C2 is the right label. An analysis of the contributions of the variables has shown that F4 is th... | [
"0.34",
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] | [
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 245 | 312 | {'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... | [
"F4",
"F7",
"F6",
"F1",
"F3",
"F5",
"F2",
"F9",
"F8"
] | {'F4': 'CD Account', 'F7': 'Income', 'F6': 'CCAvg', 'F1': 'Securities Account', 'F3': 'Education', 'F5': 'Family', 'F2': 'Mortgage', 'F9': 'Age', 'F8': 'Extra_service'} | {'F8': 'F4', 'F2': 'F7', 'F4': 'F6', 'F7': 'F1', 'F5': 'F3', 'F3': 'F5', 'F6': 'F2', 'F1': 'F9', 'F9': 'F8'} | {'C1': 'C2', 'C2': 'C1'} | Reject | {'C2': 'Reject', 'C1': 'Accept'} |
KNeighborsClassifier | C1 | Real Estate Investment | Based on the information available about the case under consideration, the classification model is very uncertain about the appropriate labels for the case. According to the model, there is an almost equal distribution in terms of the probability that any one of C1 and C2 is an appropriate label. This indicates that an... | [
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"negative",
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] | 185 | 358 | {'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: F16 and F11.",
"Summarize th... | [
"F16",
"F11",
"F18",
"F9",
"F13",
"F6",
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"F1",
"F12",
"F19",
"F4",
"F20",
"F10",
"F15",
"F5",
"F17",
"F3",
"F14",
"F8",
"F2"
] | {'F16': 'Feature7', 'F11': 'Feature4', 'F18': 'Feature2', 'F9': 'Feature8', 'F13': 'Feature20', 'F6': 'Feature1', 'F7': 'Feature12', 'F1': 'Feature15', 'F12': 'Feature6', 'F19': 'Feature9', 'F4': 'Feature17', 'F20': 'Feature3', 'F10': 'Feature19', 'F15': 'Feature13', 'F5': 'Feature18', 'F17': 'Feature5', 'F3': 'Feature... | {'F11': 'F16', 'F9': 'F11', 'F1': 'F18', 'F3': 'F9', 'F20': 'F13', 'F7': 'F6', 'F15': 'F7', 'F4': 'F1', 'F10': 'F12', 'F12': 'F19', 'F6': 'F4', 'F8': 'F20', 'F5': 'F10', 'F16': 'F15', 'F19': 'F5', 'F2': 'F17', 'F14': 'F3', 'F18': 'F14', 'F13': 'F8', 'F17': 'F2'} | {'C1': 'C1', 'C2': 'C2'} | Ignore | {'C1': 'Ignore', 'C2': 'Invest'} |
LogisticRegression | C2 | Tic-Tac-Toe Strategy | There is about an 81.01% chance that C2 is the probable label, hence the predicted probability for the C1 class is only 18.99%. The algorithm or classifier arrived at the prediction verdict above mainly based on the influence of features such as F9, F4, F8, and F6. For the algorithm, the least relevant feature is F5, ... | [
"0.28",
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"0.25",
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] | [
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative"
] | 231 | 138 | {'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... | [
"F9",
"F4",
"F8",
"F6",
"F1",
"F3",
"F7",
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"F5"
] | {'F9': 'bottom-right-square', 'F4': 'middle-middle-square', 'F8': 'bottom-left-square', 'F6': 'middle-left-square', 'F1': 'top-left-square', 'F3': ' top-right-square', 'F7': 'middle-right-square', 'F2': 'top-middle-square', 'F5': 'bottom-middle-square'} | {'F9': 'F9', 'F5': 'F4', 'F7': 'F8', 'F4': 'F6', 'F1': 'F1', 'F3': 'F3', 'F6': 'F7', 'F2': 'F2', 'F8': 'F5'} | {'C1': 'C1', 'C2': 'C2'} | player B win | {'C1': 'player B lose', 'C2': 'player B win'} |
BernoulliNB | C1 | Hotel Satisfaction | According to the classification algorithm, there is 77.69% chance that the given case is part of the C1 population. The features with the largest impact driving the algorithm to arrive at the above decision are F8, F11, and F2 which are followed in the decreasing order of influence by F3, F7, F9, F5, F14, F4, F15, F12,... | [
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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 (F8 (with a value equal to V0), F11 (value equal to V0), F2 and F3) on the prediction made for this test case.",
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] | {'F8': 'Type of Travel', 'F11': 'Type Of Booking', 'F2': 'Common Room entertainment', 'F3': 'Other service', 'F7': 'Stay comfort', 'F9': 'Cleanliness', 'F5': 'Hotel wifi service', 'F14': 'Ease of Online booking', 'F4': 'Checkin\\/Checkout service', 'F15': 'Age', 'F12': 'Food and drink', 'F13': 'Hotel location', 'F6': '... | {'F3': 'F8', 'F4': 'F11', 'F12': 'F2', 'F14': 'F3', 'F11': 'F7', 'F15': 'F9', 'F6': 'F5', 'F8': 'F14', 'F13': 'F4', 'F5': 'F15', 'F10': 'F12', 'F9': 'F13', 'F7': 'F6', 'F2': 'F1', 'F1': 'F10'} | {'C1': 'C1', 'C2': 'C2'} | dissatisfied | {'C1': 'dissatisfied', 'C2': 'satisfied'} |
DecisionTreeClassifier | C1 | Insurance Churn | The likelihood of the true label for the given test case being equal to the model's output prediction, C1, is 85.71% and since it's not 100%, there is a small chance of about 14.29% that the model could be wrong. Among the features employed for this classification, F12, F6, F9, F1, F3, and F16 are the top features infl... | [
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] | 79 | 29 | {'C2': '14.29%', 'C1': '85.71%'} | [
"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: F12 (equal to V2) and F6 (whe... | [
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] | {'F12': 'feature15', 'F6': 'feature7', 'F9': 'feature10', 'F1': 'feature11', 'F3': 'feature5', 'F16': 'feature13', 'F4': 'feature3', 'F8': 'feature4', 'F7': 'feature12', 'F11': 'feature14', 'F2': 'feature1', 'F5': 'feature6', 'F13': 'feature2', 'F10': 'feature9', 'F15': 'feature8', 'F14': 'feature0'} | {'F9': 'F12', 'F1': 'F6', 'F4': 'F9', 'F5': 'F1', 'F15': 'F3', 'F7': 'F16', 'F13': 'F4', 'F14': 'F8', 'F6': 'F7', 'F8': 'F11', 'F11': 'F2', 'F16': 'F5', 'F12': 'F13', 'F3': 'F10', 'F2': 'F15', 'F10': 'F14'} | {'C1': 'C2', 'C2': 'C1'} | Leave | {'C2': 'Stay', 'C1': 'Leave'} |
SVC | C1 | Vehicle Insurance Claims | To begin with, the classification choice is entirely dependent on the information or data provided to the prediction model. According to the model, C1 has a 61.61 percent probability of being the true label, whereas C2 has a 38.39 percent chance of being the true label. Because the estimated probability of C1 is greate... | [
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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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LGBMClassifier | C1 | Employee Promotion Prediction | With a prediction likelihood of 62.34%, the model trained to generate predictions based on input variables identifies the presented example as C1. The model's label assignment choice for the given case is heavily impacted by the values of input variables such as F4, F2, and F8. The least important variables, on the oth... | [
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"negative",
"negative",
"positive",
"positive",
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] | 106 | 345 | {'C2': '37.66%', 'C1': '62.34%'} | [
"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, F10 and F9) on the model’s prediction of C1.",
"Summarize the set of ... | [
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] | {'F2': 'department', 'F4': 'avg_training_score', 'F8': 'recruitment_channel', 'F11': 'KPIs_met >80%', 'F10': 'no_of_trainings', 'F9': 'length_of_service', 'F6': 'age', 'F7': 'region', 'F5': 'education', 'F3': 'previous_year_rating', 'F1': 'gender'} | {'F1': 'F2', 'F11': 'F4', 'F5': 'F8', 'F10': 'F11', 'F6': 'F10', 'F9': 'F9', 'F7': 'F6', 'F2': 'F7', 'F3': 'F5', 'F8': 'F3', 'F4': 'F1'} | {'C2': 'C2', 'C1': 'C1'} | Promote | {'C2': 'Ignore', 'C1': 'Promote'} |
SVC | C2 | Food Ordering Customer Churn Prediction | For the case under consideration, the model outputs C2 with high confidence level since the associated predicted class label is 89.73% whilst that of C1 is just 10.27%. Just few features out of the entire input features are shown to have control over the prediction made here. The prediction verdict C2 is mainly based o... | [
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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: F28 and F15.",
"Summarize th... | [
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RandomForestClassifier | C2 | Health Care Services Satisfaction Prediction | The model trained to solve the classification task labels the given case as C2 with a moderately high degree of confidence level equal to 60.13%. However, it is important to note that the prediction likelihood of C1 is 39.87%. Investigation of the contributions of the features to the above label assignment indicates th... | [
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] | 192 | 442 | {'C2': '60.13%', 'C1': '39.87%'} | [
"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: F5, F1, F8 and F14.",
"Compare and contrast the impact of the following features (F10, F9 and F13) on the model’s prediction of C2.",
"Descr... | [
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] | {'F5': 'Communication with dr', 'F1': 'Quality\\/experience dr.', 'F8': 'Time of appointment', 'F14': 'Specialists avaliable', 'F10': 'Modern equipment', 'F9': 'parking, playing rooms, caffes', 'F13': 'waiting rooms', 'F3': 'Admin procedures', 'F12': 'hospital rooms quality', 'F4': 'Check up appointment', 'F2': 'Exact ... | {'F8': 'F5', 'F6': 'F1', 'F5': 'F8', 'F7': 'F14', 'F10': 'F10', 'F16': 'F9', 'F14': 'F13', 'F3': 'F3', 'F15': 'F12', 'F1': 'F4', 'F9': 'F2', 'F11': 'F16', 'F2': 'F15', 'F12': 'F6', 'F13': 'F11', 'F4': 'F7'} | {'C2': 'C2', 'C1': 'C1'} | Dissatisfied | {'C2': 'Dissatisfied', 'C1': 'Satisfied'} |
DNN | C2 | Concrete Strength Classification | The following assertions are based on the information provided to the classification model. The classification model's confidence in this case's prediction output is approximately 69.40% and this suggest that the chance of label C1 is about 30.60%. The prediction attribution analysis shows that F5 and F2 are the most i... | [
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"positive",
"positive",
"negative",
"negative",
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] | 269 | 356 | {'C2': '69.40%', 'C1': '30.60%'} | [
"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",
"F2",
"F7",
"F1",
"F8",
"F3",
"F4",
"F6"
] | {'F5': 'slag', 'F2': 'water', 'F7': 'cement', 'F1': 'fineaggregate', 'F8': 'flyash', 'F3': 'coarseaggregate', 'F4': 'age_days', 'F6': 'superplasticizer'} | {'F2': 'F5', 'F4': 'F2', 'F1': 'F7', 'F7': 'F1', 'F3': 'F8', 'F6': 'F3', 'F8': 'F4', 'F5': 'F6'} | {'C2': 'C2', 'C1': 'C1'} | Weak | {'C2': 'Weak', 'C1': 'Strong'} |
GradientBoostingClassifier | C1 | Broadband Sevice Signup | Because the chance that the label is the alternative class C2 is only 1.94 percent, the model anticipates that C1 will be the correct label in this situation. Specifically, it can be concluded that the model has a high level of confidence in the label C1. The feature attribution analysis conducted suggests that the two... | [
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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: F10 and F1.",
"Compare and contrast the impact of the following features (F12, F32, F23 (with a value equal to V1) and F8) on the model’s pre... | [
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RandomForestClassifier | C1 | Student Job Placement | The model predicted that the example should be classified as C1 with a 76.06% likelihood but the model also identified that there was a 23.94% chance that the right label could actually be C2. The positive influence of features F1, F2, F3, and F8 on the model supports the class assignment of C1. Both F9 and F5 are feat... | [
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"positive",
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] | 19 | 6 | {'C1': '76.06%', 'C2': '23.94%'} | [
"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, F2, F3 (with a value equal to V0) and F8 (equal to V1).",
"Compare and contrast the impact of the following features (F9 (with a value e... | [
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"F6"
] | {'F1': 'ssc_p', 'F2': 'hsc_p', 'F3': 'workex', 'F8': 'specialisation', 'F9': 'gender', 'F4': 'hsc_s', 'F5': 'degree_p', 'F11': 'etest_p', 'F12': 'degree_t', 'F7': 'ssc_b', 'F10': 'hsc_b', 'F6': 'mba_p'} | {'F1': 'F1', 'F2': 'F2', 'F11': 'F3', 'F12': 'F8', 'F6': 'F9', 'F9': 'F4', 'F3': 'F5', 'F4': 'F11', 'F10': 'F12', 'F7': 'F7', 'F8': 'F10', 'F5': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | Not Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
RandomForestClassifier | C1 | Used Cars Price-Range Prediction | The classification model labels the given case as C1 at a very high confidence level since the probability that C2 is the correct label according to the model is only 3.50%. The assignment decision above is mainly based on the values of the features F6, F8, F9, and F3. On the other hand, the values of F1 and F4 are sho... | [
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] | 183 | 106 | {'C2': '3.50%', 'C1': '96.50%'} | [
"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... | [
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"F1",
"F4"
] | {'F6': 'Fuel_Type', 'F8': 'Transmission', 'F9': 'Power', 'F3': 'Kilometers_Driven', 'F5': 'Mileage', 'F2': 'car_age', 'F7': 'Engine', 'F10': 'Seats', 'F1': 'Owner_Type', 'F4': 'Name'} | {'F7': 'F6', 'F8': 'F8', 'F4': 'F9', 'F1': 'F3', 'F2': 'F5', 'F5': 'F2', 'F3': 'F7', 'F10': 'F10', 'F9': 'F1', 'F6': 'F4'} | {'C2': 'C2', 'C1': 'C1'} | High | {'C2': 'Low', 'C1': 'High'} |
SVM_linear | C2 | Employee Promotion Prediction | The model generated the label, C2, with a very high likelihood of 99.69%, hence the probability that C1 is the right label is only 0.31%. Based on the analysis performed to understand the attributions of the different features, F5 was by far the most impactful positive feature whereas, the most negative feature is iden... | [
"0.54",
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] | [
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
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] | 100 | 46 | {'C1': '0.31%', 'C2': '99.69%'} | [
"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 (F6, F2 (with a value equal to V2), F9 and F1) on the model’s prediction of ... | [
"F5",
"F7",
"F6",
"F2",
"F9",
"F1",
"F3",
"F11",
"F4",
"F10",
"F8"
] | {'F5': 'avg_training_score', 'F7': 'department', 'F6': 'KPIs_met >80%', 'F2': 'recruitment_channel', 'F9': 'age', 'F1': 'no_of_trainings', 'F3': 'previous_year_rating', 'F11': 'education', 'F4': 'region', 'F10': 'length_of_service', 'F8': 'gender'} | {'F11': 'F5', 'F1': 'F7', 'F10': 'F6', 'F5': 'F2', 'F7': 'F9', 'F6': 'F1', 'F8': 'F3', 'F3': 'F11', 'F2': 'F4', 'F9': 'F10', 'F4': 'F8'} | {'C2': 'C1', 'C1': 'C2'} | Promote | {'C1': 'Ignore', 'C2': 'Promote'} |
KNeighborsClassifier | C1 | Advertisement Prediction | The item is labelled as C1 with a high degree of confidence since the predicted probability associated with the other class is 0.0%. Looking at the contributions of the features, only F7 and F1, are shown to drive the model towards predicting C2. However, these features are ranked as the least relevant, implying that t... | [
"0.42",
"0.27",
"0.16",
"0.06",
"0.05",
"-0.03",
"-0.03"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 191 | 111 | {'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... | [
"F5",
"F6",
"F3",
"F4",
"F2",
"F7",
"F1"
] | {'F5': 'Daily Internet Usage', 'F6': 'Daily Time Spent on Site', 'F3': 'Age', 'F4': 'Area Income', 'F2': 'ad_day', 'F7': 'ad_month', 'F1': 'Gender'} | {'F4': 'F5', 'F1': 'F6', 'F2': 'F3', 'F3': 'F4', 'F7': 'F2', 'F6': 'F7', 'F5': 'F1'} | {'C2': 'C1', 'C1': 'C2'} | Skip | {'C1': 'Skip', 'C2': 'Watch'} |
SVC_linear | C1 | Personal Loan Modelling | With the prediction probability distribution across the labels, C2 and C1, respectively, equal to 0.30% and 99.70%, the model labels this instance as C1. The most important features are F9, F7, and F8. The variables, F6, F2, F4, and F5, have values, increasing the chances of C2 being the label for this case. Increasing... | [
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"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
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] | 161 | 87 | {'C2': '0.30%', 'C1': '99.70%'} | [
"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: F9, F7, F8, F6 and F1.",
"Compare and contrast the impact of the following features (F2, F4 and F3) on the model’s prediction of C1.",
"Desc... | [
"F9",
"F7",
"F8",
"F6",
"F1",
"F2",
"F4",
"F3",
"F5"
] | {'F9': 'Income', 'F7': 'CD Account', 'F8': 'Education', 'F6': 'Family', 'F1': 'Securities Account', 'F2': 'CCAvg', 'F4': 'Mortgage', 'F3': 'Extra_service', 'F5': 'Age'} | {'F2': 'F9', 'F8': 'F7', 'F5': 'F8', 'F3': 'F6', 'F7': 'F1', 'F4': 'F2', 'F6': 'F4', 'F9': 'F3', 'F1': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Accept | {'C2': 'Reject', 'C1': 'Accept'} |
DecisionTreeClassifier | C3 | Car Acceptability Valuation | C3 is given as the predicted label with very high confidence, and according to the classification algorithm, there is no chance that either of the remaining three labels, C1, C3, and C2, is the right label for this case since the predicted probability of C4 is 100.0%. Based on the attribution analysis and investigation... | [
"0.42",
"-0.24",
"-0.11",
"-0.09",
"-0.05",
"-0.04"
] | [
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 18 | 5 | {'C4': '100.00%', 'C1': '0.00%', 'C2': '0.0%', 'C3': '0.0%'} | [
"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",
"F3",
"F1",
"F5"
] | {'F4': 'safety', 'F6': 'persons', 'F2': 'buying', 'F3': 'maint', 'F1': 'lug_boot', 'F5': 'doors'} | {'F6': 'F4', 'F4': 'F6', 'F1': 'F2', 'F2': 'F3', 'F5': 'F1', 'F3': 'F5'} | {'C2': 'C4', 'C4': 'C1', 'C1': 'C2', 'C3': 'C3'} | Unacceptable | {'C4': 'Other B', 'C1': 'Acceptable', 'C2': 'Other A', 'C3': 'Unacceptable'} |
KNeighborsClassifier | C1 | German Credit Evaluation | In the present case, there is only a 12.50% chance that C2 is the correct label, which means there is an 87.50% chance that C1 is the true label. Therefore, the most probable class assigned by the model is C1. The above decision is mainly based on the influence of the following variables: F4, F9, and F6. Of these main ... | [
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"-0.08",
"-0.06",
"-0.06",
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"0.04",
"0.01",
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] | [
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 167 | 220 | {'C1': '87.50%', 'C2': '12.50%'} | [
"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... | [
"F9",
"F6",
"F4",
"F1",
"F7",
"F2",
"F8",
"F5",
"F3"
] | {'F9': 'Checking account', 'F6': 'Saving accounts', 'F4': 'Purpose', 'F1': 'Sex', 'F7': 'Duration', 'F2': 'Housing', 'F8': 'Age', 'F5': 'Job', 'F3': 'Credit amount'} | {'F6': 'F9', 'F5': 'F6', 'F9': 'F4', 'F2': 'F1', 'F8': 'F7', 'F4': 'F2', 'F1': 'F8', 'F3': 'F5', 'F7': 'F3'} | {'C2': 'C1', 'C1': 'C2'} | Good Credit | {'C1': 'Good Credit', 'C2': 'Bad Credit'} |
KNeighborsClassifier | C1 | Tic-Tac-Toe Strategy | There is an evenly split chance that the prediction could be either of the two labels, C1 and C2. Based on the predicted probabilities, we can conclude that the model is uncertain about which label is the correct one. The abovementioned prediction decision is chiefly attributed to the influence of the following feature... | [
"0.21",
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"-0.09",
"-0.06",
"-0.04",
"0.04",
"-0.03",
"0.02",
"0.01"
] | [
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive"
] | 212 | 125 | {'C1': '50.00%', 'C2': '50.00%'} | [
"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... | [
"F9",
"F2",
"F6",
"F4",
"F3",
"F5",
"F7",
"F8",
"F1"
] | {'F9': 'middle-middle-square', 'F2': 'top-left-square', 'F6': 'bottom-left-square', 'F4': 'bottom-right-square', 'F3': 'top-middle-square', 'F5': ' top-right-square', 'F7': 'middle-right-square', 'F8': 'bottom-middle-square', 'F1': 'middle-left-square'} | {'F5': 'F9', 'F1': 'F2', 'F7': 'F6', 'F9': 'F4', 'F2': 'F3', 'F3': 'F5', 'F6': 'F7', 'F8': 'F8', 'F4': 'F1'} | {'C1': 'C1', 'C2': 'C2'} | player B lose | {'C1': 'player B lose', 'C2': 'player B win'} |
SVC | C2 | Water Quality Classification | The label assigned to the given sample is C2 at a confidence level of 56.81%. This means that there is a 43.19% chance that the sample could be C1, representing an uncertain classification decision. The values of F5, F3, F1, F4, and F8 are the major contributing factors resulting in the classification decision here. On... | [
"-0.01",
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"0.01",
"0.01",
"0.01",
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] | [
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative"
] | 188 | 441 | {'C2': '56.81%', 'C1': '43.19%'} | [
"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... | [
"F5",
"F3",
"F1",
"F4",
"F8",
"F2",
"F6",
"F9",
"F7"
] | {'F5': 'ph', 'F3': 'Conductivity', 'F1': 'Sulfate', 'F4': 'Hardness', 'F8': 'Turbidity', 'F2': 'Solids', 'F6': 'Chloramines', 'F9': 'Trihalomethanes', 'F7': 'Organic_carbon'} | {'F1': 'F5', 'F6': 'F3', 'F5': 'F1', 'F2': 'F4', 'F9': 'F8', 'F3': 'F2', 'F4': 'F6', 'F8': 'F9', 'F7': 'F7'} | {'C1': 'C2', 'C2': 'C1'} | Not Portable | {'C2': 'Not Portable', 'C1': 'Portable'} |
SVC | C2 | Australian Credit Approval | The classification algorithm classifies the given case as C2, since there is only an 18.57% chance that C1 is the correct label. The effects and contributions of positive input variables F2, F6, and F14 are the major drivers for the above classification. Besides, most of the remaining predictors such as F4, F3, F8, F12... | [
"0.43",
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"positive",
"positive",
"negative",
"positive",
"negative",
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] | 244 | 315 | {'C1': '18.57%', 'C2': '81.43%'} | [
"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",
"F6",
"F14",
"F4",
"F3",
"F8",
"F12",
"F13",
"F11",
"F1",
"F5",
"F10",
"F9",
"F7"
] | {'F2': 'A8', 'F6': 'A9', 'F14': 'A14', 'F4': 'A12', 'F3': 'A7', 'F8': 'A4', 'F12': 'A5', 'F13': 'A11', 'F11': 'A1', 'F1': 'A13', 'F5': 'A10', 'F10': 'A2', 'F9': 'A6', 'F7': 'A3'} | {'F8': 'F2', 'F9': 'F6', 'F14': 'F14', 'F12': 'F4', 'F7': 'F3', 'F4': 'F8', 'F5': 'F12', 'F11': 'F13', 'F1': 'F11', 'F13': 'F1', 'F10': 'F5', 'F2': 'F10', 'F6': 'F9', 'F3': 'F7'} | {'C1': 'C1', 'C2': 'C2'} | Class 2 | {'C1': 'Class 1', 'C2': 'Class 2'} |
LogisticRegression | C1 | Concrete Strength Classification | The odds are in favour of C1 being the correct label for the given case. This is because the probability of the other label, C2, is only 1.03%. Ranking the features in order of relevance to the classification decision above, F1, F7, F3, F2, F5, F4, F6, and F8. Among the set of features used for this prediction, F7, F5,... | [
"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 | 102 | {'C2': '1.03%', 'C1': '98.97%'} | [
"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: F1, F7 and F3.",
"Summarize ... | [
"F1",
"F7",
"F3",
"F2",
"F5",
"F4",
"F6",
"F8"
] | {'F1': 'cement', 'F7': 'age_days', 'F3': 'water', 'F2': 'superplasticizer', 'F5': 'fineaggregate', 'F4': 'flyash', 'F6': 'slag', 'F8': 'coarseaggregate'} | {'F1': 'F1', 'F8': 'F7', 'F4': 'F3', 'F5': 'F2', 'F7': 'F5', 'F3': 'F4', 'F2': 'F6', 'F6': 'F8'} | {'C1': 'C2', 'C2': 'C1'} | Strong | {'C2': 'Weak', 'C1': 'Strong'} |
GradientBoostingClassifier | C2 | Paris House Classification | According to the prediction algorithm or model, there is almost 100% confidence that C2 is the label for the case under consideration. This is because the probability of C1 being the correct label is only 0.70%. The classification decision above is mainly based on the values of the following features: F10, F12, and F8 ... | [
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"negative",
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] | 154 | 82 | {'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 (F8, F9, F1 and F16) on the model’s prediction of C2.",
"Summarize the set ... | [
"F10",
"F12",
"F8",
"F9",
"F1",
"F16",
"F4",
"F17",
"F14",
"F11",
"F5",
"F7",
"F3",
"F15",
"F13",
"F6",
"F2"
] | {'F10': 'isNewBuilt', 'F12': 'hasYard', 'F8': 'hasPool', 'F9': 'hasStormProtector', 'F1': 'made', 'F16': 'hasGuestRoom', 'F4': 'squareMeters', 'F17': 'floors', 'F14': 'cityCode', 'F11': 'basement', 'F5': 'price', 'F7': 'numPrevOwners', 'F3': 'numberOfRooms', 'F15': 'attic', 'F13': 'cityPartRange', 'F6': 'garage', 'F2':... | {'F3': 'F10', 'F1': 'F12', 'F2': 'F8', 'F4': 'F9', 'F12': 'F1', 'F16': 'F16', 'F6': 'F4', 'F8': 'F17', 'F9': 'F14', 'F13': 'F11', 'F17': 'F5', 'F11': 'F7', 'F7': 'F3', 'F14': 'F15', 'F10': 'F13', 'F15': 'F6', 'F5': 'F2'} | {'C2': 'C2', 'C1': 'C1'} | Basic | {'C2': 'Basic', 'C1': 'Luxury'} |
MLPClassifier | C1 | Ethereum Fraud Detection | The C2 has a predicted probability of just 3.10 percent, but the C1 has a predicted probability of 96.90 percent, which implies that C1 is the most likely class chosen by the classifier for the supplied data. Not all of the input features are directly relevant to labelling the provided data and, per the attributions an... | [
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"neg... | 243 | 316 | {'C2': '3.10%', 'C1': '96.90%'} | [
"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",
"F12",
"F11",
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"F3",
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"F26",
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"... | {'F1': 'Unique Received From Addresses', 'F12': ' ERC20 total Ether sent contract', 'F11': 'total ether received', 'F21': 'Sent tnx', 'F3': 'Number of Created Contracts', 'F14': ' ERC20 uniq rec token name', 'F26': ' ERC20 uniq rec contract addr', 'F13': 'max value received ', 'F4': 'total transactions (including tnx t... | {'F7': 'F1', 'F26': 'F12', 'F20': 'F11', 'F4': 'F21', 'F6': 'F3', 'F38': 'F14', 'F30': 'F26', 'F10': 'F13', 'F18': 'F4', 'F29': 'F34', 'F27': 'F23', 'F5': 'F27', 'F11': 'F29', 'F28': 'F17', 'F14': 'F5', 'F9': 'F22', 'F8': 'F9', 'F37': 'F25', 'F2': 'F35', 'F3': 'F31', 'F31': 'F19', 'F32': 'F28', 'F34': 'F24', 'F35': 'F1... | {'C2': 'C2', 'C1': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
RandomForestClassifier | C1 | German Credit Evaluation | The classification algorithm labels this instance as C1, but its level of confidence is moderate considering the fact that there is about a 44.0% chance that C2 could be the appropriate label. The features, F9, F7, F5, and F4, negatively influence the prediction verdict away from C1 and favour assigning C2 as the corr... | [
"-0.10",
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"-0.06",
"0.05",
"-0.03",
"0.02",
"0.02",
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] | [
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive"
] | 229 | 136 | {'C1': '56.00%', 'C2': '44.00%'} | [
"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... | [
"F9",
"F1",
"F7",
"F8",
"F5",
"F2",
"F6",
"F4",
"F3"
] | {'F9': 'Saving accounts', 'F1': 'Sex', 'F7': 'Duration', 'F8': 'Purpose', 'F5': 'Housing', 'F2': 'Age', 'F6': 'Checking account', 'F4': 'Credit amount', 'F3': 'Job'} | {'F5': 'F9', 'F2': 'F1', 'F8': 'F7', 'F9': 'F8', 'F4': 'F5', 'F1': 'F2', 'F6': 'F6', 'F7': 'F4', 'F3': 'F3'} | {'C1': 'C1', 'C2': 'C2'} | Good Credit | {'C1': 'Good Credit', 'C2': 'Bad Credit'} |
GradientBoostingClassifier | C2 | Food Ordering Customer Churn Prediction | The case given is labelled as C2 with close to an 82.07% confidence level, implying that the likelihood of C1 being the correct label is only 17.93%. The classification above is mainly due to the contributions of different features such as F21, F8, F3, F24, F19, and F33. But, not all features are considered by the clas... | [
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"negative",
"negligible",
"negligible",
"neg... | 7 | 361 | {'C1': '17.93%', 'C2': '82.07%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F21 (when it is equal to V1), F8 (value equal to V1), F3 (equal to V0), F24 (when it is equal to V1) and F19 (when it is equal to V3)) on the prediction m... | [
"F21",
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... | {'F21': 'More restaurant choices', 'F8': 'Ease and convenient', 'F3': 'Bad past experience', 'F24': 'Time saving', 'F19': 'Unaffordable', 'F33': 'Educational Qualifications', 'F37': 'Late Delivery', 'F39': 'Occupation', 'F16': 'Influence of rating', 'F40': 'Less Delivery time', 'F25': 'Order placed by mistake', 'F42': ... | {'F12': 'F21', 'F10': 'F8', 'F21': 'F3', 'F11': 'F24', 'F23': 'F19', 'F6': 'F33', 'F19': 'F37', 'F4': 'F39', 'F38': 'F16', 'F39': 'F40', 'F29': 'F25', 'F37': 'F42', 'F31': 'F29', 'F22': 'F46', 'F14': 'F45', 'F26': 'F10', 'F45': 'F20', 'F27': 'F32', 'F43': 'F5', 'F28': 'F15', 'F33': 'F9', 'F34': 'F11', 'F1': 'F30', 'F35... | {'C1': 'C1', 'C2': 'C2'} | Go Away | {'C1': 'Return', 'C2': 'Go Away'} |
RandomForestClassifier | C2 | Company Bankruptcy Prediction | The output labelling decision is C2 with almost 100% certainty, which indicates that there is practically no chance that C1 is the right label choice for the case under consideration. F71, F21, F70, F8, and F51 are the features with the highest joint positive impact, influencing the model's decision to output C2 and t... | [
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"neg... | 54 | 21 | {'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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"F23... | {'F71': " Net Income to Stockholder's Equity", 'F21': ' Continuous interest rate (after tax)', 'F70': ' ROA(C) before interest and depreciation before interest', 'F18': ' Borrowing dependency', 'F8': ' Cash Flow Per Share', 'F51': ' Net worth\\/Assets', 'F24': ' Total income\\/Total expense', 'F46': ' Persistent EPS in... | {'F59': 'F71', 'F12': 'F21', 'F29': 'F70', 'F3': 'F18', 'F65': 'F8', 'F84': 'F51', 'F57': 'F24', 'F8': 'F46', 'F10': 'F63', 'F27': 'F82', 'F53': 'F45', 'F42': 'F73', 'F35': 'F50', 'F78': 'F33', 'F31': 'F68', 'F18': 'F66', 'F72': 'F28', 'F23': 'F84', 'F89': 'F12', 'F34': 'F32', 'F87': 'F13', 'F64': 'F52', 'F67': 'F30', ... | {'C1': 'C2', 'C2': 'C1'} | No | {'C2': 'No', 'C1': 'Yes'} |
LogisticRegression | C1 | House Price Classification | The label assigned by the classifier in this instance is C1, which had a very high prediction likelihood of about 99.93%. According to this classifier, the probability of C2 being the correct class is only 0.07%. Analysis performed shows that the confidence level of the classifier here is due to mainly the values of th... | [
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"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
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] | 198 | 113 | {'C2': '0.07%', 'C1': '99.93%'} | [
"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",
"F4",
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"F10",
"F5",
"F7",
"F8",
"F11",
"F13",
"F12",
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"F2",
"F6"
] | {'F3': 'LSTAT', 'F4': 'RM', 'F9': 'PTRATIO', 'F10': 'RAD', 'F5': 'CHAS', 'F7': 'TAX', 'F8': 'CRIM', 'F11': 'DIS', 'F13': 'AGE', 'F12': 'B', 'F1': 'ZN', 'F2': 'NOX', 'F6': 'INDUS'} | {'F13': 'F3', 'F6': 'F4', 'F11': 'F9', 'F9': 'F10', 'F4': 'F5', 'F10': 'F7', 'F1': 'F8', 'F8': 'F11', 'F7': 'F13', 'F12': 'F12', 'F2': 'F1', 'F5': 'F2', 'F3': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | High | {'C2': 'Low', 'C1': 'High'} |
BernoulliNB | C2 | Credit Card Fraud Classification | The classifier is very certain that C1 is not the accurate label for the given data or example, but that C2 fits. F1, F9, F17, F15, F14, F27, and F2 are the input features that have the most influence on the choice or judgment. F23, F5, F24, F6, F22, F20, F28, F12, F8, and F29, on the other hand, are found to be irrele... | [
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"negligible",
"negligible",
"neg... | 239 | 324 | {'C2': '100.00%', '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... | [
"F1",
"F9",
"F17",
"F15",
"F14",
"F2",
"F27",
"F10",
"F26",
"F30",
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"F22",
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] | {'F1': 'Z4', 'F9': 'Z3', 'F17': 'Z23', 'F15': 'Z2', 'F14': 'Z10', 'F2': 'Z7', 'F27': 'Z12', 'F10': 'Z14', 'F26': 'Z24', 'F30': 'Z28', 'F13': 'Time', 'F3': 'Z19', 'F11': 'Z26', 'F25': 'Z16', 'F19': 'Z5', 'F4': 'Z22', 'F16': 'Amount', 'F7': 'Z9', 'F18': 'Z18', 'F21': 'Z15', 'F23': 'Z17', 'F5': 'Z1', 'F24': 'Z20', 'F6': '... | {'F5': 'F1', 'F4': 'F9', 'F24': 'F17', 'F3': 'F15', 'F11': 'F14', 'F8': 'F2', 'F13': 'F27', 'F15': 'F10', 'F25': 'F26', 'F29': 'F30', 'F1': 'F13', 'F20': 'F3', 'F27': 'F11', 'F17': 'F25', 'F6': 'F19', 'F23': 'F4', 'F30': 'F16', 'F10': 'F7', 'F19': 'F18', 'F16': 'F21', 'F18': 'F23', 'F2': 'F5', 'F21': 'F24', 'F22': 'F6'... | {'C1': 'C2', 'C2': 'C1'} | Not Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
SGDClassifier | C2 | House Price Classification | The classifier's anticipated label for this case is C2 which is a decision that it is highly confident about since the predicted likelihood is 100.0%. The most important variables are F9, F12, F7, and F1, whose values lead to the aforesaid classification conclusion. Under this classification instance, examination of th... | [
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] | 143 | 227 | {'C2': '100.00%', 'C1': '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 (F9 and F12) on the prediction made for this test case.",
"Compare the direction of impact of the features: F7, F1, F2 and F8.",
"Describe the degree of imp... | [
"F9",
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"F7",
"F1",
"F2",
"F8",
"F3",
"F4",
"F5",
"F6",
"F11",
"F10",
"F13"
] | {'F9': 'CRIM', 'F12': 'LSTAT', 'F7': 'RAD', 'F1': 'AGE', 'F2': 'CHAS', 'F8': 'DIS', 'F3': 'ZN', 'F4': 'TAX', 'F5': 'PTRATIO', 'F6': 'B', 'F11': 'RM', 'F10': 'NOX', 'F13': 'INDUS'} | {'F1': 'F9', 'F13': 'F12', 'F9': 'F7', 'F7': 'F1', 'F4': 'F2', 'F8': 'F8', 'F2': 'F3', 'F10': 'F4', 'F11': 'F5', 'F12': 'F6', 'F6': 'F11', 'F5': 'F10', 'F3': 'F13'} | {'C2': 'C2', 'C1': 'C1'} | Low | {'C2': 'Low', 'C1': 'High'} |
GaussianNB | C1 | Tic-Tac-Toe Strategy | The model selects C1 as the correct label with a probability of 57.58%, while the other class, C2, has a slightly lower probability of 42.42%. The most relevant attribute is F5, followed by F8, F2, F3, F1, F9, F7, F4 and finally F6, which is the least relevant. The features F1, F7, and F5 have a positive influence, inc... | [
"0.39",
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"-0.14",
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"-0.10",
"0.07",
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] | [
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive"
] | 37 | 214 | {'C1': '57.58%', 'C2': '42.42%'} | [
"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 (when it is equal to V2) a... | [
"F5",
"F2",
"F3",
"F4",
"F8",
"F9",
"F7",
"F1",
"F6"
] | {'F5': 'middle-middle-square', 'F2': 'top-left-square', 'F3': 'bottom-right-square', 'F4': ' top-right-square', 'F8': 'middle-left-square', 'F9': 'bottom-middle-square', 'F7': 'bottom-left-square', 'F1': 'middle-right-square', 'F6': 'top-middle-square'} | {'F5': 'F5', 'F1': 'F2', 'F9': 'F3', 'F3': 'F4', 'F4': 'F8', 'F8': 'F9', 'F7': 'F7', 'F6': 'F1', 'F2': 'F6'} | {'C2': 'C1', 'C1': 'C2'} | player B lose | {'C1': 'player B lose', 'C2': 'player B win'} |
SGDClassifier | C1 | House Price Classification | The prediction verdict here is that the most probable class label is C1. Actually, the classification algorithm indicates that there is no possibility that the correct label is C2. Majorly contributing to the above classification are F2, F5, F10, and F9, all with positive influence. It is therefore not surprising tha... | [
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] | [
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"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
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"negative"
] | 273 | 180 | {'C2': '0.00%', 'C1': '100.00%'} | [
"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",
"F5",
"F10",
"F9",
"F11",
"F12",
"F13",
"F1",
"F8",
"F7",
"F3",
"F4",
"F6"
] | {'F2': 'AGE', 'F5': 'RAD', 'F10': 'LSTAT', 'F9': 'RM', 'F11': 'DIS', 'F12': 'CHAS', 'F13': 'ZN', 'F1': 'CRIM', 'F8': 'TAX', 'F7': 'B', 'F3': 'PTRATIO', 'F4': 'INDUS', 'F6': 'NOX'} | {'F7': 'F2', 'F9': 'F5', 'F13': 'F10', 'F6': 'F9', 'F8': 'F11', 'F4': 'F12', 'F2': 'F13', 'F1': 'F1', 'F10': 'F8', 'F12': 'F7', 'F11': 'F3', 'F3': 'F4', 'F5': 'F6'} | {'C2': 'C2', 'C1': 'C1'} | High | {'C2': 'Low', 'C1': 'High'} |
DecisionTreeClassifier | C1 | Hotel Satisfaction | Due to the prediction probability distribution across the class labels, the labels assigned to this example is C1 with a high degree of confidence, close to 100 percent. The most significant features driving the classification above, according to the attributions of the input features, are F12, F3, F11, and F9. F1 and ... | [
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"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 190 | 212 | {'C2': '0.00%', 'C1': '100.00%'} | [
"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, F9, F3 and F7.",
"Compare and contrast the impact of the following features (F15, F8 and F13) on the model’s prediction of C1.",
"... | [
"F12",
"F11",
"F9",
"F3",
"F7",
"F15",
"F8",
"F13",
"F14",
"F5",
"F2",
"F10",
"F4",
"F1",
"F6"
] | {'F12': 'Type of Travel', 'F11': 'Hotel wifi service', 'F9': 'Other service', 'F3': 'Type Of Booking', 'F7': 'Checkin\\/Checkout service', 'F15': 'Age', 'F8': 'purpose_of_travel', 'F13': 'Common Room entertainment', 'F14': 'Food and drink', 'F5': 'Stay comfort', 'F2': 'Hotel location', 'F10': 'Departure\\/Arrival conv... | {'F3': 'F12', 'F6': 'F11', 'F14': 'F9', 'F4': 'F3', 'F13': 'F7', 'F5': 'F15', 'F2': 'F8', 'F12': 'F13', 'F10': 'F14', 'F11': 'F5', 'F9': 'F2', 'F7': 'F10', 'F1': 'F4', 'F8': 'F1', 'F15': 'F6'} | {'C2': 'C2', 'C1': 'C1'} | satisfied | {'C2': 'dissatisfied', 'C1': 'satisfied'} |
RandomForestClassifier | C2 | Student Job Placement | The classification algorithm predicts that the data sample given should be classified as C2 with a probability of 76.06%, but it also finds that there is a 23.94% probability that the correct label will be C1. The positive influence of the F4, F10, F6, and F11 features on the algorithm supports the C2 class tasks. F8 a... | [
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] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative"
] | 19 | 310 | {'C2': '76.06%', 'C1': '23.94%'} | [
"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: F4, F10, F6 (with a value equal to V0) and F11 (equal to V1).",
"Compare and contrast the impact of the following features (F8 (with a value... | [
"F4",
"F10",
"F6",
"F11",
"F8",
"F1",
"F7",
"F5",
"F12",
"F2",
"F3",
"F9"
] | {'F4': 'ssc_p', 'F10': 'hsc_p', 'F6': 'workex', 'F11': 'specialisation', 'F8': 'gender', 'F1': 'hsc_s', 'F7': 'degree_p', 'F5': 'etest_p', 'F12': 'degree_t', 'F2': 'ssc_b', 'F3': 'hsc_b', 'F9': 'mba_p'} | {'F1': 'F4', 'F2': 'F10', 'F11': 'F6', 'F12': 'F11', 'F6': 'F8', 'F9': 'F1', 'F3': 'F7', 'F4': 'F5', 'F10': 'F12', 'F7': 'F2', 'F8': 'F3', 'F5': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | Not Placed | {'C2': 'Not Placed', 'C1': 'Placed'} |
KNeighborsClassifier | C1 | Credit Risk Classification | According to the model, there is a higher chance that the case's label is C1. This prediction decision is based primarily on the attribution of the following features: F6, F9, F3, and F10. Aside from F10, all the other features listed above have a strong positive influence, increasing the probability of the predicted c... | [
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] | [
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive"
] | 115 | 52 | {'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 (F6, F9, F3 and F10) on the prediction made for this test case.",
"Compare the direction of impact of the features: F8, F2 and F7.",
"Describe the degree of... | [
"F6",
"F9",
"F3",
"F10",
"F8",
"F2",
"F7",
"F11",
"F4",
"F1",
"F5"
] | {'F6': 'fea_4', 'F9': 'fea_8', 'F3': 'fea_2', 'F10': 'fea_9', 'F8': 'fea_6', 'F2': 'fea_10', 'F7': 'fea_1', 'F11': 'fea_7', 'F4': 'fea_11', 'F1': 'fea_3', 'F5': 'fea_5'} | {'F4': 'F6', 'F8': 'F9', 'F2': 'F3', 'F9': 'F10', 'F6': 'F8', 'F10': 'F2', 'F1': 'F7', 'F7': 'F11', 'F11': 'F4', 'F3': 'F1', 'F5': 'F5'} | {'C1': 'C1', 'C2': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
KNeighborsClassifier | C1 | Company Bankruptcy Prediction | For the case under consideration, the model's output labelling decision is as follows: there is no possibility that C2 is the label for the given case, C1 is the most likely class label, with a confidence level close of 100.0%. The values of the input features, F27, F48, F56, F76, F66, F74, and F90, are the main drivin... | [
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"negative",
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"negligible",
"negligible",
"neg... | 423 | 201 | {'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... | [
"F27",
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"F1... | {'F27': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F48': ' Net Income to Total Assets', 'F56': ' Realized Sales Gross Profit Growth Rate', 'F76': ' Accounts Receivable Turnover', 'F66': ' Operating Expense Rate', 'F90': ' Contingent liabilities\\/Net worth', 'F74': ' Non-industry income and expenditure\\/r... | {'F60': 'F27', 'F16': 'F48', 'F38': 'F56', 'F2': 'F76', 'F19': 'F66', 'F64': 'F90', 'F4': 'F74', 'F82': 'F13', 'F50': 'F34', 'F22': 'F65', 'F85': 'F78', 'F33': 'F51', 'F88': 'F83', 'F43': 'F82', 'F80': 'F60', 'F54': 'F89', 'F27': 'F54', 'F23': 'F7', 'F76': 'F42', 'F7': 'F36', 'F61': 'F87', 'F59': 'F4', 'F62': 'F32', 'F... | {'C2': 'C1', 'C1': 'C2'} | No | {'C1': 'No', 'C2': 'Yes'} |
KNeighborsClassifier | C1 | Wine Quality Prediction | Based on the influence of features such as F7, F6, F11, and F3, the classifier is pretty confident that the correct label for the given data is C1, whilst, there is a 10.0% probability that the proper label could be C2. The majority of the features have positive contributions, while only F3, F10, and F8 are the negati... | [
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] | 234 | 140 | {'C2': '10.00%', 'C1': '90.00%'} | [
"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... | [
"F7",
"F6",
"F11",
"F3",
"F9",
"F5",
"F4",
"F1",
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] | {'F7': 'sulphates', 'F6': 'total sulfur dioxide', 'F11': 'volatile acidity', 'F3': 'residual sugar', 'F9': 'citric acid', 'F5': 'chlorides', 'F4': 'alcohol', 'F1': 'fixed acidity', 'F10': 'density', 'F2': 'pH', 'F8': 'free sulfur dioxide'} | {'F10': 'F7', 'F7': 'F6', 'F2': 'F11', 'F4': 'F3', 'F3': 'F9', 'F5': 'F5', 'F11': 'F4', 'F1': 'F1', 'F8': 'F10', 'F9': 'F2', 'F6': 'F8'} | {'C2': 'C2', 'C1': 'C1'} | high quality | {'C2': 'low_quality', 'C1': 'high quality'} |
LogisticRegression | C1 | Music Concert Attendance | The model's prediction for this test case is C1 with an almost 100% confidence level which implies that the likelihood of it being a different class label is closer to 0%. Among the top influential feature-set, F5 has a value shifting the label choice in favour of C2, while the others, F17, F4, and F13, all have a posi... | [
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] | 71 | 422 | {'C1': '98.44%', 'C2': '1.56%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F17, F4, F5, F13 and F7) on the prediction made for this test case.",
"Compare the direction of impact of the features: F15, F6 and F2.",
"Describe the deg... | [
"F17",
"F4",
"F5",
"F13",
"F7",
"F15",
"F6",
"F2",
"F14",
"F20",
"F18",
"F16",
"F8",
"F11",
"F10",
"F9",
"F1",
"F3",
"F19",
"F12"
] | {'F17': 'X6', 'F4': 'X11', 'F5': 'X1', 'F13': 'X13', 'F7': 'X2', 'F15': 'X8', 'F6': 'X10', 'F2': 'X14', 'F14': 'X4', 'F20': 'X3', 'F18': 'X9', 'F16': 'X16', 'F8': 'X18', 'F11': 'X7', 'F10': 'X19', 'F9': 'X5', 'F1': 'X17', 'F3': 'X15', 'F19': 'X12', 'F12': 'X20'} | {'F6': 'F17', 'F11': 'F4', 'F1': 'F5', 'F13': 'F13', 'F2': 'F7', 'F8': 'F15', 'F10': 'F6', 'F14': 'F2', 'F4': 'F14', 'F3': 'F20', 'F9': 'F18', 'F16': 'F16', 'F18': 'F8', 'F7': 'F11', 'F19': 'F10', 'F5': 'F9', 'F17': 'F1', 'F15': 'F3', 'F12': 'F19', 'F20': 'F12'} | {'C2': 'C1', 'C1': 'C2'} | < 10k | {'C1': '< 10k', 'C2': '> 10k'} |
MLPClassifier | C1 | Ethereum Fraud Detection | The classification verdict for the selected case is C1, and the model is very certain about that considering the prediction probabilities across the possible classes. The top variables influencing this decision are F26, F2, F19, F15, and F18. Other variables that are regarded as somewhat important are F28, F32, F34, F2... | [
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"neg... | 166 | 92 | {'C1': '100.00%', 'C2': '0.00%'} | [
"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... | [
"F26",
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... | {'F26': 'Unique Received From Addresses', 'F2': ' ERC20 total Ether sent contract', 'F19': 'total ether received', 'F15': 'Number of Created Contracts', 'F18': 'Sent tnx', 'F28': ' ERC20 uniq rec token name', 'F32': ' ERC20 uniq rec contract addr', 'F34': 'max value received ', 'F27': 'total transactions (including tnx... | {'F7': 'F26', 'F26': 'F2', 'F20': 'F19', 'F6': 'F15', 'F4': 'F18', 'F38': 'F28', 'F30': 'F32', 'F10': 'F34', 'F18': 'F27', 'F29': 'F37', 'F27': 'F8', 'F5': 'F11', 'F28': 'F33', 'F14': 'F35', 'F9': 'F10', 'F8': 'F25', 'F37': 'F30', 'F23': 'F14', 'F3': 'F4', 'F2': 'F7', 'F19': 'F13', 'F12': 'F6', 'F11': 'F23', 'F36': 'F2... | {'C1': 'C1', 'C2': 'C2'} | Not Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
LogisticRegression | C1 | Employee Promotion Prediction | Classifying the given case based on the values of its features, C1 is the best label for the given case since its prediction probability is 99.45%, while C2's is just 0.55 percent. The most relevant factors for the classification or prediction declaration above are F6, F2, and F11, whereas the least influential factors... | [
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] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive"
] | 236 | 327 | {'C2': '0.55%', 'C1': '99.45%'} | [
"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",
"F2",
"F11",
"F1",
"F9",
"F8",
"F3",
"F4",
"F5",
"F10",
"F7"
] | {'F6': 'avg_training_score', 'F2': 'KPIs_met >80%', 'F11': 'department', 'F1': 'age', 'F9': 'no_of_trainings', 'F8': 'recruitment_channel', 'F3': 'previous_year_rating', 'F4': 'length_of_service', 'F5': 'education', 'F10': 'region', 'F7': 'gender'} | {'F11': 'F6', 'F10': 'F2', 'F1': 'F11', 'F7': 'F1', 'F6': 'F9', 'F5': 'F8', 'F8': 'F3', 'F9': 'F4', 'F3': 'F5', 'F2': 'F10', 'F4': 'F7'} | {'C2': 'C2', 'C1': 'C1'} | Promote | {'C2': 'Ignore', 'C1': 'Promote'} |
LogisticRegression | C2 | Concrete Strength Classification | Per the predicted likelihoods across the classes, the model predicts label C2 in this case with a high confidence level. Features F3, F4, F8, and F5 are all driving the model towards the C2 classification, with feature F3 being the strongest driver and F5 being the weak driver among the above mentioned set of features.... | [
"0.15",
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"0.13",
"0.08",
"0.08",
"-0.02",
"-0.02",
"-0.01"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative"
] | 23 | 9 | {'C2': '90.65%', 'C1': '9.35%'} | [
"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, F4, F8 and F5.",
"Compare and contrast the impact of the following features (F1, F7 and F2) on the model’s prediction of C2.",
"Describe... | [
"F3",
"F4",
"F8",
"F5",
"F1",
"F7",
"F2",
"F6"
] | {'F3': 'water', 'F4': 'cement', 'F8': 'age_days', 'F5': 'flyash', 'F1': 'superplasticizer', 'F7': 'coarseaggregate', 'F2': 'fineaggregate', 'F6': 'slag'} | {'F4': 'F3', 'F1': 'F4', 'F8': 'F8', 'F3': 'F5', 'F5': 'F1', 'F6': 'F7', 'F7': 'F2', 'F2': 'F6'} | {'C2': 'C2', 'C1': 'C1'} | Weak | {'C2': 'Weak', 'C1': 'Strong'} |
KNeighborsClassifier | C2 | E-Commerce Shipping | The classifier is very uncertain about the correct class for this example and this is because both classes are shown to be equally likely. The above prediction conclusion is mainly based on the influence of the top input features F5, F1, and F3, while F7, F9, and F6 have less influence on the classifier when classifyin... | [
"-0.12",
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"-0.02",
"0.02",
"0.01",
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] | [
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive"
] | 203 | 257 | {'C2': '50.00%', 'C1': '50.00%'} | [
"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... | [
"F5",
"F1",
"F3",
"F8",
"F10",
"F2",
"F4",
"F7",
"F9",
"F6"
] | {'F5': 'Discount_offered', 'F1': 'Weight_in_gms', 'F3': 'Prior_purchases', 'F8': 'Customer_care_calls', 'F10': 'Product_importance', 'F2': 'Mode_of_Shipment', 'F4': 'Warehouse_block', 'F7': 'Cost_of_the_Product', 'F9': 'Customer_rating', 'F6': 'Gender'} | {'F2': 'F5', 'F3': 'F1', 'F8': 'F3', 'F6': 'F8', 'F9': 'F10', 'F5': 'F2', 'F4': 'F4', 'F1': 'F7', 'F7': 'F9', 'F10': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | On-time | {'C2': 'On-time', 'C1': 'Late'} |
LogisticRegression | C1 | Airline Passenger Satisfaction | C1 is the label assigned to this data instance based on the fact that C2 is shown to be very unlikely, with a prediction probability of only 0.68%. The variables most relevant to increasing the probability of the prediction here are F7, F17, F11, and F4. Other positive features that increase the chances of predicting C... | [
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] | 162 | 288 | {'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... | [
"F11",
"F20",
"F17",
"F4",
"F7",
"F5",
"F8",
"F15",
"F12",
"F2",
"F10",
"F18",
"F1",
"F6",
"F14",
"F13",
"F3",
"F22",
"F19",
"F9",
"F21",
"F16"
] | {'F11': 'Type of Travel', 'F20': 'Customer Type', 'F17': 'Inflight entertainment', 'F4': 'Inflight wifi service', 'F7': 'Departure\\/Arrival time convenient', 'F5': 'Gate location', 'F8': 'Arrival Delay in Minutes', 'F15': 'Seat comfort', 'F12': 'Online boarding', 'F2': 'Ease of Online booking', 'F10': 'Class', 'F18': ... | {'F4': 'F11', 'F2': 'F20', 'F14': 'F17', 'F7': 'F4', 'F8': 'F7', 'F10': 'F5', 'F22': 'F8', 'F13': 'F15', 'F12': 'F12', 'F9': 'F2', 'F5': 'F10', 'F3': 'F18', 'F15': 'F1', 'F20': 'F6', 'F18': 'F14', 'F19': 'F13', 'F11': 'F3', 'F21': 'F22', 'F17': 'F19', 'F1': 'F9', 'F6': 'F21', 'F16': 'F16'} | {'C1': 'C1', 'C2': 'C2'} | neutral or dissatisfied | {'C1': 'neutral or dissatisfied', 'C2': 'satisfied'} |
BernoulliNB | C1 | Customer Churn Modelling | C1 is the class assigned to this case or instance. However, according to the classifier, there is a 5.75% chance that the other label, C2, is the correct one. The labelling decision above is mainly due to the values F2, F6, and F1. F7 and F10 are the least ranked features since they have marginal attributions. F6, F5, ... | [
"0.22",
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] | [
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 172 | 96 | {'C1': '94.25%', 'C2': '5.75%'} | [
"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 F6.",
"Compare and contrast the impact of the following features (F1, F4, F9 and F3) on the model’s prediction of C1.",
"Describe the... | [
"F2",
"F6",
"F1",
"F4",
"F9",
"F3",
"F5",
"F8",
"F7",
"F10"
] | {'F2': 'IsActiveMember', 'F6': 'NumOfProducts', 'F1': 'Gender', 'F4': 'Geography', 'F9': 'Age', 'F3': 'CreditScore', 'F5': 'EstimatedSalary', 'F8': 'Balance', 'F7': 'HasCrCard', 'F10': 'Tenure'} | {'F9': 'F2', 'F7': 'F6', 'F3': 'F1', 'F2': 'F4', 'F4': 'F9', 'F1': 'F3', 'F10': 'F5', 'F6': 'F8', 'F8': 'F7', 'F5': 'F10'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Stay', 'C2': 'Leave'} |
LogisticRegression | C2 | Real Estate Investment | The model predicts the class label of this test case or instance as C2 and it is quite confident in the above prediction decision considering the predicted confidence level. The above prediction decision was made primarily based on the values of the following features: F11, F16, F10, and F5. The top features, F11 and F... | [
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"positive",
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"negative",
"negative",
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"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative"
] | 77 | 27 | {'C1': '2.40%', 'C2': '97.60%'} | [
"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",
"F16",
"F10",
"F5",
"F18",
"F17",
"F8",
"F6",
"F15",
"F20",
"F7",
"F13",
"F1",
"F3",
"F2",
"F9",
"F12",
"F4",
"F19",
"F14"
] | {'F11': 'Feature7', 'F16': 'Feature4', 'F10': 'Feature2', 'F5': 'Feature14', 'F18': 'Feature15', 'F17': 'Feature8', 'F8': 'Feature20', 'F6': 'Feature1', 'F15': 'Feature17', 'F20': 'Feature3', 'F7': 'Feature16', 'F13': 'Feature18', 'F1': 'Feature10', 'F3': 'Feature5', 'F2': 'Feature6', 'F9': 'Feature12', 'F12': 'Feature... | {'F11': 'F11', 'F9': 'F16', 'F1': 'F10', 'F17': 'F5', 'F4': 'F18', 'F3': 'F17', 'F20': 'F8', 'F7': 'F6', 'F6': 'F15', 'F8': 'F20', 'F18': 'F7', 'F19': 'F13', 'F13': 'F1', 'F2': 'F3', 'F10': 'F2', 'F15': 'F9', 'F5': 'F12', 'F16': 'F4', 'F12': 'F19', 'F14': 'F14'} | {'C1': 'C1', 'C2': 'C2'} | Invest | {'C1': 'Ignore', 'C2': 'Invest'} |
DecisionTreeClassifier | C2 | Concrete Strength Classification | The case is labelled as C2 by the classification model, and according to the model, there is little to no chance that the correct label could be C1. Per the feature attribution inspection, F3 and F2 are the least influential features. The classification decision to label this case as C2 is mainly due to the positive co... | [
"-0.32",
"0.30",
"0.16",
"0.10",
"-0.07",
"-0.03",
"0.03",
"-0.02"
] | [
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative"
] | 184 | 439 | {'C1': '0.00%', 'C2': '100.00%'} | [
"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",
"F8",
"F1",
"F6",
"F5",
"F7",
"F3",
"F2"
] | {'F4': 'cement', 'F8': 'age_days', 'F1': 'water', 'F6': 'superplasticizer', 'F5': 'coarseaggregate', 'F7': 'fineaggregate', 'F3': 'flyash', 'F2': 'slag'} | {'F1': 'F4', 'F8': 'F8', 'F4': 'F1', 'F5': 'F6', 'F6': 'F5', 'F7': 'F7', 'F3': 'F3', 'F2': 'F2'} | {'C1': 'C1', 'C2': 'C2'} | Strong | {'C1': 'Weak', 'C2': 'Strong'} |
SVC | C2 | Real Estate Investment | The decision of the classification model on the true label with respect to the given case is based on the information provided to it. From the prediction probabilities, C2 is selected by the model as the most likely label, with a very high confidence level equal to 97.49%. According to the attributions analysis, the ve... | [
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"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive"
] | 438 | 464 | {'C1': '2.51%', 'C2': '97.49%'} | [
"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",
"F20",
"F10",
"F17",
"F12",
"F9",
"F13",
"F3",
"F8",
"F5",
"F2",
"F11",
"F19",
"F14",
"F18",
"F7",
"F1",
"F15",
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"F4"
] | {'F6': 'Feature7', 'F20': 'Feature4', 'F10': 'Feature14', 'F17': 'Feature2', 'F12': 'Feature3', 'F9': 'Feature8', 'F13': 'Feature13', 'F3': 'Feature15', 'F8': 'Feature1', 'F5': 'Feature11', 'F2': 'Feature9', 'F11': 'Feature16', 'F19': 'Feature12', 'F14': 'Feature18', 'F18': 'Feature19', 'F7': 'Feature5', 'F1': 'Feature... | {'F11': 'F6', 'F9': 'F20', 'F17': 'F10', 'F1': 'F17', 'F8': 'F12', 'F3': 'F9', 'F16': 'F13', 'F4': 'F3', 'F7': 'F8', 'F14': 'F5', 'F12': 'F2', 'F18': 'F11', 'F15': 'F19', 'F19': 'F14', 'F5': 'F18', 'F2': 'F7', 'F10': 'F1', 'F13': 'F15', 'F20': 'F16', 'F6': 'F4'} | {'C2': 'C1', 'C1': 'C2'} | Invest | {'C1': 'Ignore', 'C2': 'Invest'} |
KNeighborsClassifier | C2 | Cab Surge Pricing System | With a moderate likelihood of 50.0%, the label for this case is judged to be C2. The classifier, on the other hand, says that C3 and C1 are equally likely, with a predicted probability of 25.0 percent. The aforementioned decision is mostly dependent on the features of the given case and the values of F10, F11, and F8 a... | [
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] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative"
] | 60 | 416 | {'C3': '25.00%', 'C1': '25.00%', 'C2': '50.00%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F10 (when it is equal to V0) and F11) on the prediction made for this test case.",
"Compare the direction of impact of the features: F8, F2, F7 and F5.",
... | [
"F10",
"F11",
"F8",
"F2",
"F7",
"F5",
"F9",
"F3",
"F1",
"F12",
"F6",
"F4"
] | {'F10': 'Destination_Type', 'F11': 'Cancellation_Last_1Month', 'F8': 'Trip_Distance', 'F2': 'Customer_Rating', 'F7': 'Var1', 'F5': 'Life_Style_Index', 'F9': 'Confidence_Life_Style_Index', 'F3': 'Var3', 'F1': 'Customer_Since_Months', 'F12': 'Gender', 'F6': 'Var2', 'F4': 'Type_of_Cab'} | {'F6': 'F10', 'F8': 'F11', 'F1': 'F8', 'F7': 'F2', 'F9': 'F7', 'F4': 'F5', 'F5': 'F9', 'F11': 'F3', 'F3': 'F1', 'F12': 'F12', 'F10': 'F6', 'F2': 'F4'} | {'C1': 'C3', 'C2': 'C1', 'C3': 'C2'} | C3 | {'C3': 'Low', 'C1': 'Medium', 'C2': 'High'} |
LogisticRegression | C1 | Music Concert Attendance | With a prediction probability of around 82.06 percent, the algorithm predicts class C1. In the aforementioned prediction judgment, F8, F1, F4, and F9 are all important. The top positively contributing features supporting the C1 prediction are F8, F1, and F9, while F4 is pushing the final prediction away. F15 also has a... | [
"0.29",
"0.27",
"-0.22",
"0.13",
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"0.04",
"-0.04",
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"-0.03",
"0.03",
"-0.03",
"0.02",
"0.02",
"-0.02",
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] | [
"positive",
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"positive",
"negative",
"positive",
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"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive"
] | 46 | 294 | {'C2': '17.94%', 'C1': '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 (F9, F2 and F15) on the model’s prediction of C1.",
"Summarize the set of f... | [
"F8",
"F1",
"F4",
"F9",
"F2",
"F15",
"F3",
"F17",
"F10",
"F11",
"F6",
"F20",
"F12",
"F19",
"F14",
"F16",
"F5",
"F7",
"F18",
"F13"
] | {'F8': 'X11', 'F1': 'X1', 'F4': 'X13', 'F9': 'X3', 'F2': 'X8', 'F15': 'X6', 'F3': 'X2', 'F17': 'X9', 'F10': 'X17', 'F11': 'X10', 'F6': 'X4', 'F20': 'X14', 'F12': 'X20', 'F19': 'X18', 'F14': 'X19', 'F16': 'X7', 'F5': 'X12', 'F7': 'X15', 'F18': 'X16', 'F13': 'X5'} | {'F11': 'F8', 'F1': 'F1', 'F13': 'F4', 'F3': 'F9', 'F8': 'F2', 'F6': 'F15', 'F2': 'F3', 'F9': 'F17', 'F17': 'F10', 'F10': 'F11', 'F4': 'F6', 'F14': 'F20', 'F20': 'F12', 'F18': 'F19', 'F19': 'F14', 'F7': 'F16', 'F12': 'F5', 'F15': 'F7', 'F16': 'F18', 'F5': 'F13'} | {'C2': 'C2', 'C1': 'C1'} | > 10k | {'C2': '< 10k', 'C1': '> 10k'} |
LogisticRegression | C1 | House Price Classification | For this test case, the model predicts C1 with 99.93% certainty and what this means is that there is only 0.07% chance that C2 could be the right one. The features with the highest impact are F6, F9, F7, and F5, which are all shown to contribute positively to the prediction decision mentioned above. While F4 and F13 su... | [
"0.35",
"0.27",
"0.21",
"0.18",
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"0.07",
"0.07",
"0.06",
"-0.04",
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"-0.02",
"0.01",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive"
] | 38 | 12 | {'C2': '0.07%', 'C1': '99.93%'} | [
"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, F4 and F13) on the model’s prediction of C1.",
"Summarize the set of ... | [
"F6",
"F9",
"F7",
"F5",
"F11",
"F4",
"F13",
"F8",
"F3",
"F10",
"F2",
"F1",
"F12"
] | {'F6': 'LSTAT', 'F9': 'RM', 'F7': 'PTRATIO', 'F5': 'RAD', 'F11': 'CHAS', 'F4': 'TAX', 'F13': 'CRIM', 'F8': 'DIS', 'F3': 'AGE', 'F10': 'B', 'F2': 'ZN', 'F1': 'NOX', 'F12': 'INDUS'} | {'F13': 'F6', 'F6': 'F9', 'F11': 'F7', 'F9': 'F5', 'F4': 'F11', 'F10': 'F4', 'F1': 'F13', 'F8': 'F8', 'F7': 'F3', 'F12': 'F10', 'F2': 'F2', 'F5': 'F1', 'F3': 'F12'} | {'C1': 'C2', 'C2': 'C1'} | High | {'C2': 'Low', 'C1': 'High'} |
BernoulliNB | C2 | Employee Promotion Prediction | This model trained on eleven attributes predicts class label C2 for this case with a confidence level equal to 54.21%. This suggests that the likelihood of C1 being the correct label is 45.79%. The classification decision above is mainly based on the influence of the features F11, F8, F3, and F4. The most relevant feat... | [
"-0.32",
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"-0.08",
"0.07",
"0.04",
"0.03",
"-0.02",
"-0.02",
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] | [
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive"
] | 157 | 84 | {'C2': '54.21%', 'C1': '45.79%'} | [
"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",
"F8",
"F3",
"F4",
"F10",
"F9",
"F7",
"F6",
"F5",
"F2",
"F1"
] | {'F11': 'KPIs_met >80%', 'F8': 'previous_year_rating', 'F3': 'avg_training_score', 'F4': 'department', 'F10': 'education', 'F9': 'recruitment_channel', 'F7': 'no_of_trainings', 'F6': 'length_of_service', 'F5': 'region', 'F2': 'age', 'F1': 'gender'} | {'F10': 'F11', 'F8': 'F8', 'F11': 'F3', 'F1': 'F4', 'F3': 'F10', 'F5': 'F9', 'F6': 'F7', 'F9': 'F6', 'F2': 'F5', 'F7': 'F2', 'F4': 'F1'} | {'C2': 'C2', 'C1': 'C1'} | Ignore | {'C2': 'Ignore', 'C1': 'Promote'} |
LogisticRegression | C2 | Used Cars Price-Range Prediction | With a moderate confidence level of 67.95%, the model predicts C2 for the case under consideration, but it is important to consider the fact that there is a 32.05% chance that C1 could be the correct label instead. The most influential variables resulting in the aforementioned classification decision are F1, F9, and F1... | [
"-0.21",
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"0.09",
"-0.04",
"-0.04",
"0.04",
"0.02",
"-0.01",
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"-0.00"
] | [
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative"
] | 20 | 279 | {'C1': '32.05%', 'C2': '67.95%'} | [
"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... | [
"F1",
"F9",
"F10",
"F7",
"F8",
"F6",
"F2",
"F5",
"F4",
"F3"
] | {'F1': 'Fuel_Type', 'F9': 'Seats', 'F10': 'car_age', 'F7': 'Name', 'F8': 'Owner_Type', 'F6': 'Power', 'F2': 'Engine', 'F5': 'Transmission', 'F4': 'Mileage', 'F3': 'Kilometers_Driven'} | {'F7': 'F1', 'F10': 'F9', 'F5': 'F10', 'F6': 'F7', 'F9': 'F8', 'F4': 'F6', 'F3': 'F2', 'F8': 'F5', 'F2': 'F4', 'F1': 'F3'} | {'C1': 'C1', 'C2': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
LogisticRegression | C1 | Employee Promotion Prediction | As per the classification algorithm, the most appropriate label for the given case is C1 because its prediction likelihood is 99.45%, whereas that of C2 is only 0.55%. For the classification or prediction assertion above, the most important variables are F7, F6, and F9, while the least influential variables are F3, F8... | [
"0.54",
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"-0.01",
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] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive"
] | 236 | 142 | {'C2': '0.55%', 'C1': '99.45%'} | [
"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",
"F6",
"F9",
"F4",
"F10",
"F2",
"F5",
"F3",
"F8",
"F1",
"F11"
] | {'F7': 'avg_training_score', 'F6': 'KPIs_met >80%', 'F9': 'department', 'F4': 'age', 'F10': 'no_of_trainings', 'F2': 'recruitment_channel', 'F5': 'previous_year_rating', 'F3': 'length_of_service', 'F8': 'education', 'F1': 'region', 'F11': 'gender'} | {'F11': 'F7', 'F10': 'F6', 'F1': 'F9', 'F7': 'F4', 'F6': 'F10', 'F5': 'F2', 'F8': 'F5', 'F9': 'F3', 'F3': 'F8', 'F2': 'F1', 'F4': 'F11'} | {'C1': 'C2', 'C2': 'C1'} | Promote | {'C2': 'Ignore', 'C1': 'Promote'} |
SGDClassifier | C2 | House Price Classification | C2 is the label predicted by the classification model employed and looking at the prediction probabilities, it valid to concluded that the model is very certain about the selected label. The features considered most relevant by the model for the above decision are F12, F10, F7, and F3, while those with the least consid... | [
"0.38",
"0.30",
"-0.27",
"0.26",
"0.16",
"-0.14",
"0.11",
"0.07",
"0.07",
"-0.07",
"0.06",
"0.03",
"0.01"
] | [
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive"
] | 143 | 226 | {'C2': '100.00%', 'C1': '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 (F12 and F7) on the prediction made for this test case.",
"Compare the direction of impact of the features: F10, F3, F1 and F13.",
"Describe the degree of i... | [
"F12",
"F7",
"F10",
"F3",
"F1",
"F13",
"F8",
"F6",
"F11",
"F2",
"F5",
"F4",
"F9"
] | {'F12': 'CRIM', 'F7': 'LSTAT', 'F10': 'RAD', 'F3': 'AGE', 'F1': 'CHAS', 'F13': 'DIS', 'F8': 'ZN', 'F6': 'TAX', 'F11': 'PTRATIO', 'F2': 'B', 'F5': 'RM', 'F4': 'NOX', 'F9': 'INDUS'} | {'F1': 'F12', 'F13': 'F7', 'F9': 'F10', 'F7': 'F3', 'F4': 'F1', 'F8': 'F13', 'F2': 'F8', 'F10': 'F6', 'F11': 'F11', 'F12': 'F2', 'F6': 'F5', 'F5': 'F4', 'F3': 'F9'} | {'C2': 'C2', 'C1': 'C1'} | Low | {'C2': 'Low', 'C1': 'High'} |
LogisticRegression | C2 | E-Commerce Shipping | The confidence level for the prediction made for the given case is 71.57%. F6 has a significant impact on the outcome in the negative. The values F9, F1, F3, F7, F5, F10, and F4 all have a positive impact on the results, but they are still less than the effects of F6. The analysis shows that F6 has the highest impact o... | [
"-0.25",
"0.08",
"0.04",
"0.02",
"0.01",
"0.01",
"0.01",
"0.00",
"-0.00",
"-0.00"
] | [
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 70 | 23 | {'C2': '71.57%', 'C1': '28.43%'} | [
"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 (F1 (with a value equal to V4), F3 (when it is equal to V2), F7 and F5 (whe... | [
"F6",
"F9",
"F1",
"F3",
"F7",
"F5",
"F10",
"F4",
"F2",
"F8"
] | {'F6': 'Discount_offered', 'F9': 'Weight_in_gms', 'F1': 'Prior_purchases', 'F3': 'Product_importance', 'F7': 'Cost_of_the_Product', 'F5': 'Gender', 'F10': 'Customer_rating', 'F4': 'Warehouse_block', 'F2': 'Customer_care_calls', 'F8': 'Mode_of_Shipment'} | {'F2': 'F6', 'F3': 'F9', 'F8': 'F1', 'F9': 'F3', 'F1': 'F7', 'F10': 'F5', 'F7': 'F10', 'F4': 'F4', 'F6': 'F2', 'F5': 'F8'} | {'C2': 'C2', 'C1': 'C1'} | On-time | {'C2': 'On-time', 'C1': 'Late'} |
LogisticRegression | C2 | E-Commerce Shipping | 53.78% and 46.22%, respectively, are the chance or likelihood of any of the classes C2, and C1 being the appropriate label for the case given here. As a result, it's safe to say that C2 is the most likely label for this situation and F6 is identified as the most influential feature whereas F9, F2, and F4 have very low ... | [
"0.25",
"-0.08",
"0.06",
"-0.02",
"-0.01",
"0.01",
"-0.01",
"0.01",
"0.00",
"-0.00"
] | [
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative"
] | 452 | 409 | {'C1': '46.22%', 'C2': '53.78%'} | [
"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",
"F7",
"F1",
"F5",
"F8",
"F10",
"F3",
"F9",
"F2",
"F4"
] | {'F6': 'Discount_offered', 'F7': 'Weight_in_gms', 'F1': 'Prior_purchases', 'F5': 'Product_importance', 'F8': 'Cost_of_the_Product', 'F10': 'Gender', 'F3': 'Customer_rating', 'F9': 'Customer_care_calls', 'F2': 'Mode_of_Shipment', 'F4': 'Warehouse_block'} | {'F2': 'F6', 'F3': 'F7', 'F8': 'F1', 'F9': 'F5', 'F1': 'F8', 'F10': 'F10', 'F7': 'F3', 'F6': 'F9', 'F5': 'F2', 'F4': 'F4'} | {'C1': 'C1', 'C2': 'C2'} | Late | {'C1': 'On-time', 'C2': 'Late'} |
LogisticRegression | C2 | Used Cars Price-Range Prediction | The output decision for the provided data is C2, with a very high confidence level, based on the output prediction probabilities across the two classes since C1 has a probability of around 0.00%. F4, F2, and F1 are the most influential factors in the above-mentioned label assignment, however F3 and F7 are the least inf... | [
"0.53",
"0.32",
"0.18",
"0.15",
"0.13",
"0.05",
"-0.04",
"-0.03",
"-0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive"
] | 362 | 357 | {'C2': '100.00%', '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... | [
"F2",
"F4",
"F1",
"F8",
"F9",
"F5",
"F10",
"F6",
"F3",
"F7"
] | {'F2': 'car_age', 'F4': 'Power', 'F1': 'Fuel_Type', 'F8': 'Engine', 'F9': 'Seats', 'F5': 'Transmission', 'F10': 'Kilometers_Driven', 'F6': 'Name', 'F3': 'Mileage', 'F7': 'Owner_Type'} | {'F5': 'F2', 'F4': 'F4', 'F7': 'F1', 'F3': 'F8', 'F10': 'F9', 'F8': 'F5', 'F1': 'F10', 'F6': 'F6', 'F2': 'F3', 'F9': 'F7'} | {'C1': 'C2', 'C2': 'C1'} | Low | {'C2': 'Low', 'C1': 'High'} |
SVC | C1 | Advertisement Prediction | When given the task of labelling the given case one of the possible labels, C1 and C2, the model assigns C1 as the most likely correct label, with a confidence level of roughly 99.90%. This degree of confidence indicates that the likelihood of C2 being the right designation is merely 0.10%. According to the attribution... | [
"0.43",
"0.25",
"0.13",
"0.07",
"0.07",
"-0.03",
"-0.02"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 42 | 399 | {'C1': '99.90%', 'C2': '0.10%'} | [
"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",
"F3",
"F4",
"F6",
"F7",
"F2",
"F1"
] | {'F5': 'Daily Internet Usage', 'F3': 'Daily Time Spent on Site', 'F4': 'Age', 'F6': 'ad_day', 'F7': 'Area Income', 'F2': 'Gender', 'F1': 'ad_month'} | {'F4': 'F5', 'F1': 'F3', 'F2': 'F4', 'F7': 'F6', 'F3': 'F7', 'F5': 'F2', 'F6': 'F1'} | {'C2': 'C1', 'C1': 'C2'} | Skip | {'C1': 'Skip', 'C2': 'Watch'} |
LogisticRegression | C1 | Concrete Strength Classification | According to the classification model employed here, the most probable label for the given case is C1 with a confidence level equal to 98.97%. Per the attributions analysis, F8 and F1 are the most significant and influential features driving label selection. The least ranked features are F4 and F7, while F2, F3, F5, a... | [
"0.40",
"-0.24",
"0.14",
"0.12",
"-0.10",
"-0.08",
"0.02",
"0.00"
] | [
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive"
] | 411 | 198 | {'C2': '1.03%', 'C1': '98.97%'} | [
"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",
"F1",
"F2",
"F3",
"F5",
"F6",
"F4",
"F7"
] | {'F8': 'cement', 'F1': 'age_days', 'F2': 'water', 'F3': 'superplasticizer', 'F5': 'fineaggregate', 'F6': 'flyash', 'F4': 'slag', 'F7': 'coarseaggregate'} | {'F1': 'F8', 'F8': 'F1', 'F4': 'F2', 'F5': 'F3', 'F7': 'F5', 'F3': 'F6', 'F2': 'F4', 'F6': 'F7'} | {'C1': 'C2', 'C2': 'C1'} | Strong | {'C2': 'Weak', 'C1': 'Strong'} |
KNeighborsClassifier | C1 | Wine Quality Prediction | The classifier is quite sure that the right label for the data given is C1 based on the influence of variables such as F1, F7, F6, and F9. There is a 10.0% chance that the correct label is C2 and per the attributions examination conducted, the bulk of the traits contribute positively, with only three contributing negat... | [
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] | 234 | 329 | {'C2': '10.00%', 'C1': '90.00%'} | [
"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... | [
"F1",
"F7",
"F6",
"F9",
"F2",
"F11",
"F5",
"F10",
"F8",
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"F4"
] | {'F1': 'sulphates', 'F7': 'total sulfur dioxide', 'F6': 'volatile acidity', 'F9': 'residual sugar', 'F2': 'citric acid', 'F11': 'chlorides', 'F5': 'alcohol', 'F10': 'fixed acidity', 'F8': 'density', 'F3': 'pH', 'F4': 'free sulfur dioxide'} | {'F10': 'F1', 'F7': 'F7', 'F2': 'F6', 'F4': 'F9', 'F3': 'F2', 'F5': 'F11', 'F11': 'F5', 'F1': 'F10', 'F8': 'F8', 'F9': 'F3', 'F6': 'F4'} | {'C1': 'C2', 'C2': 'C1'} | high quality | {'C2': 'low_quality', 'C1': 'high quality'} |
DecisionTreeClassifier | C1 | Vehicle Insurance Claims | C1 was assigned to the given case by the classifier with a likelihood of 93.32%, leaving thhe likelihood of the C2 equal to only 6.68%. The most influential features were F16, F21, and F31. The remaining features with non-zero attributions are F3, F23, F14, F28, F10, F24, F25, F7, F11, F1, F9, F15, F20, F6, F27, and fi... | [
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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: F16 (with a value equal to V1... | [
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] | {'F16': 'incident_severity', 'F21': 'incident_city', 'F31': 'injury_claim', 'F3': 'insured_occupation', 'F23': 'insured_zip', 'F14': 'authorities_contacted', 'F28': 'auto_year', 'F10': 'police_report_available', 'F24': 'bodily_injuries', 'F25': 'insured_hobbies', 'F7': 'insured_sex', 'F11': 'auto_make', 'F1': 'property... | {'F27': 'F16', 'F30': 'F21', 'F14': 'F31', 'F22': 'F3', 'F6': 'F23', 'F28': 'F14', 'F17': 'F28', 'F32': 'F10', 'F11': 'F24', 'F23': 'F25', 'F20': 'F7', 'F33': 'F11', 'F31': 'F1', 'F12': 'F9', 'F24': 'F15', 'F2': 'F12', 'F16': 'F6', 'F1': 'F20', 'F15': 'F27', 'F25': 'F30', 'F7': 'F2', 'F3': 'F32', 'F4': 'F19', 'F29': 'F... | {'C2': 'C1', 'C1': 'C2'} | Not Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
AdaBoostClassifier | C1 | Basketball Players Career Length Prediction | With moderately high confidence, the classifier indicates that the most probable label for the given data is C1 with only just a 21.80% chance that it could be C2. The main driving features for the above classification or prediction decision are F14 and F17. The remaining features such as F1, F3, F10, and F8 have moder... | [
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] | 256 | 166 | {'C1': '78.20%', 'C2': '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... | [
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"F6",
"F13",
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] | {'F14': 'GamesPlayed', 'F17': 'PointsPerGame', 'F1': 'Steals', 'F3': 'MinutesPlayed', 'F10': 'DefensiveRebounds', 'F8': 'Rebounds', 'F2': 'Blocks', 'F12': 'FreeThrowAttempt', 'F7': 'FieldGoalPercent', 'F5': 'FreeThrowMade', 'F16': 'OffensiveRebounds', 'F9': 'FieldGoalsMade', 'F6': '3PointAttempt', 'F13': 'FreeThrowPerc... | {'F1': 'F14', 'F3': 'F17', 'F17': 'F1', 'F2': 'F3', 'F14': 'F10', 'F15': 'F8', 'F18': 'F2', 'F11': 'F12', 'F6': 'F7', 'F10': 'F5', 'F13': 'F16', 'F4': 'F9', 'F8': 'F6', 'F12': 'F13', 'F7': 'F18', 'F5': 'F15', 'F19': 'F11', 'F16': 'F19', 'F9': 'F4'} | {'C1': 'C1', 'C2': 'C2'} | More than 5 | {'C1': 'More than 5', 'C2': 'Less than 5'} |
GradientBoostingClassifier | C1 | Health Care Services Satisfaction Prediction | Given the fact that the likelihood of C2 being the correct label for the case under consideration is only 36.34%, the model assigns the label C1. The prediction decision between the two classes is highly based on the values of the features F8, F10, F2, and F7, whereas those with the least attributions or contributions... | [
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"positive",
"positive",
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] | 147 | 76 | {'C1': '63.66%', 'C2': '36.34%'} | [
"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",
"F10",
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"F5",
"F4",
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"F3",
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"F13",
"F9",
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"F1"
] | {'F8': 'Communication with dr', 'F10': 'Modern equipment', 'F2': 'Specialists avaliable', 'F7': 'Quality\\/experience dr.', 'F6': 'Time waiting', 'F16': 'Admin procedures', 'F5': 'Hygiene and cleaning', 'F4': 'waiting rooms', 'F12': 'avaliablity of drugs', 'F3': 'Time of appointment', 'F14': 'hospital rooms quality', '... | {'F8': 'F8', 'F10': 'F10', 'F7': 'F2', 'F6': 'F7', 'F2': 'F6', 'F3': 'F16', 'F4': 'F5', 'F14': 'F4', 'F13': 'F12', 'F5': 'F3', 'F15': 'F14', 'F9': 'F13', 'F16': 'F9', 'F11': 'F11', 'F1': 'F15', 'F12': 'F1'} | {'C2': 'C1', 'C1': 'C2'} | Dissatisfied | {'C1': 'Dissatisfied', 'C2': 'Satisfied'} |
KNeighborsClassifier | C1 | Cab Surge Pricing System | C1, out of the three potential classes, is the the label assigned with a high probability of 50.0%. However, the classifier indicates that C3 and C2 are equally likely, with a predicted probability of 25.0%. The aforementioned judgement is mostly based on the variables of the given case. The variables F2, F9, and F3 ar... | [
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"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
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] | 60 | 415 | {'C3': '25.00%', 'C2': '25.00%', 'C1': '50.00%'} | [
"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 (when it is equal to V0) and F9) on the prediction made for this test case.",
"Compare the direction of impact of the features: F3, F8, F11 and F5.",
"... | [
"F2",
"F9",
"F3",
"F8",
"F11",
"F5",
"F12",
"F7",
"F10",
"F4",
"F1",
"F6"
] | {'F2': 'Destination_Type', 'F9': 'Cancellation_Last_1Month', 'F3': 'Trip_Distance', 'F8': 'Customer_Rating', 'F11': 'Var1', 'F5': 'Life_Style_Index', 'F12': 'Confidence_Life_Style_Index', 'F7': 'Var3', 'F10': 'Customer_Since_Months', 'F4': 'Gender', 'F1': 'Var2', 'F6': 'Type_of_Cab'} | {'F6': 'F2', 'F8': 'F9', 'F1': 'F3', 'F7': 'F8', 'F9': 'F11', 'F4': 'F5', 'F5': 'F12', 'F11': 'F7', 'F3': 'F10', 'F12': 'F4', 'F10': 'F1', 'F2': 'F6'} | {'C2': 'C3', 'C1': 'C2', 'C3': 'C1'} | C3 | {'C3': 'Low', 'C2': 'Medium', 'C1': 'High'} |
LogisticRegression | C3 | Flight Price-Range Classification | The chances of selecting the correct label from one of the possible labels C1, C2, and C3 are 18.51%, 5.86%, and 75.63%, respectively. As a result, it can be deduced that the classifier's anticipated label in this situation is C3. The values of the input features were used as the basis to make the aforementioned predic... | [
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] | [
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative"
] | 134 | 230 | {'C2': '5.86%', 'C1': '18.51%', 'C3': '75.63%'} | [
"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: F7 and F10.",
"Compare and contrast the impact of the following features (F5, F12, F3 and F6) on the model’s prediction of C3.",
"Describe t... | [
"F7",
"F10",
"F5",
"F12",
"F3",
"F6",
"F4",
"F11",
"F9",
"F2",
"F8",
"F1"
] | {'F7': 'Total_Stops', 'F10': 'Airline', 'F5': 'Journey_day', 'F12': 'Source', 'F3': 'Destination', 'F6': 'Journey_month', 'F4': 'Dep_hour', 'F11': 'Arrival_minute', 'F9': 'Arrival_hour', 'F2': 'Duration_hours', 'F8': 'Dep_minute', 'F1': 'Duration_mins'} | {'F12': 'F7', 'F9': 'F10', 'F1': 'F5', 'F10': 'F12', 'F11': 'F3', 'F2': 'F6', 'F3': 'F4', 'F6': 'F11', 'F5': 'F9', 'F7': 'F2', 'F4': 'F8', 'F8': 'F1'} | {'C2': 'C2', 'C1': 'C1', 'C3': 'C3'} | High | {'C2': 'Low', 'C1': 'Moderate', 'C3': 'High'} |
GradientBoostingClassifier | C2 | Broadband Sevice Signup | In this case, the model expects C2 to be a label since the probability that the label is the alternative class C1 is only 1.94%. This means that the model has a lot of confidence in the selected label, C2. F31 and F32 are the two most important prediction variables positively controlling the assignment of C2 in this ca... | [
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"neg... | 117 | 235 | {'C2': '98.06%', 'C1': '1.94%'} | [
"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: F34 and F32.",
"Compare and contrast the impact of the following features (F29, F17, F26 (with a value equal to V1) and F23) on the model’s p... | [
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... | {'F34': 'X38', 'F32': 'X22', 'F29': 'X32', 'F17': 'X19', 'F26': 'X1', 'F23': 'X13', 'F3': 'X11', 'F39': 'X3', 'F40': 'X16', 'F7': 'X2', 'F30': 'X12', 'F10': 'X14', 'F15': 'X42', 'F4': 'X18', 'F12': 'X28', 'F31': 'X35', 'F6': 'X24', 'F2': 'X20', 'F41': 'X8', 'F24': 'X40', 'F25': 'X34', 'F18': 'X5', 'F11': 'X4', 'F5': 'X... | {'F35': 'F34', 'F20': 'F32', 'F29': 'F29', 'F17': 'F17', 'F40': 'F26', 'F11': 'F23', 'F9': 'F3', 'F2': 'F39', 'F14': 'F40', 'F1': 'F7', 'F10': 'F30', 'F12': 'F10', 'F38': 'F15', 'F16': 'F4', 'F26': 'F12', 'F32': 'F31', 'F22': 'F6', 'F18': 'F2', 'F6': 'F41', 'F37': 'F24', 'F31': 'F25', 'F41': 'F18', 'F3': 'F11', 'F39': ... | {'C1': 'C2', 'C2': 'C1'} | No | {'C2': 'No', 'C1': 'Yes'} |
KNeighborsClassifier | C2 | Credit Risk Classification | The following classification assertions are based on the information provided on the case under consideration. The most probable or likely label judged by the classifier is C2 since its prediction probability is 60.0% compared to the 40.0% of C1. The influence of the features on the classifier's decision here can be ra... | [
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] | [
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative"
] | 9 | 363 | {'C1': '40.00%', 'C2': '60.00%'} | [
"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",
"F11",
"F3",
"F9",
"F6",
"F10",
"F2",
"F5",
"F8",
"F7",
"F1"
] | {'F4': 'fea_4', 'F11': 'fea_8', 'F3': 'fea_2', 'F9': 'fea_9', 'F6': 'fea_6', 'F10': 'fea_10', 'F2': 'fea_1', 'F5': 'fea_11', 'F8': 'fea_7', 'F7': 'fea_3', 'F1': 'fea_5'} | {'F4': 'F4', 'F8': 'F11', 'F2': 'F3', 'F9': 'F9', 'F6': 'F6', 'F10': 'F10', 'F1': 'F2', 'F11': 'F5', 'F7': 'F8', 'F3': 'F7', 'F5': 'F1'} | {'C2': 'C1', 'C1': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
SGDClassifier | C2 | Airline Passenger Satisfaction | At a confidence level of 100.0%, the model labels this case as C2 and what this indicate is that there is no chance for C1 to be the correct label given the values of the input features. The above classification decision can be attributed to values for features such as F8, F20, F5, F10, F15, and F1. For this C2 predict... | [
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] | 140 | 70 | {'C1': '0.00%', 'C2': '100.00%'} | [
"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",
"F20",
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"F15",
"F1",
"F19",
"F12",
"F11",
"F6",
"F2",
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"F13",
"F22",
"F4",
"F16",
"F7",
"F17",
"F18",
"F14",
"F21",
"F3"
] | {'F8': 'Inflight wifi service', 'F20': 'Type of Travel', 'F5': 'Customer Type', 'F10': 'Online boarding', 'F15': 'On-board service', 'F1': 'Baggage handling', 'F19': 'Inflight service', 'F12': 'Departure\\/Arrival time convenient', 'F11': 'Leg room service', 'F6': 'Inflight entertainment', 'F2': 'Seat comfort', 'F9': '... | {'F7': 'F8', 'F4': 'F20', 'F2': 'F5', 'F12': 'F10', 'F15': 'F15', 'F17': 'F1', 'F19': 'F19', 'F8': 'F12', 'F16': 'F11', 'F14': 'F6', 'F13': 'F2', 'F5': 'F9', 'F21': 'F13', 'F20': 'F22', 'F10': 'F4', 'F1': 'F16', 'F22': 'F7', 'F3': 'F17', 'F9': 'F18', 'F6': 'F14', 'F11': 'F21', 'F18': 'F3'} | {'C2': 'C1', 'C1': 'C2'} | satisfied | {'C1': 'neutral or dissatisfied', 'C2': 'satisfied'} |
SVC | C2 | German Credit Evaluation | For the case under consideration here, there is a 70.83% probability that the true label is C2 and what this means is that there is also a 29.71% chance that C1 could be the correct label. Among the features, the top two most impactful are F4 and F8. The next features, ranked in order of the magnitude of their respecti... | [
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"-0.05",
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] | [
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive"
] | 136 | 67 | {'C2': '70.83%', 'C1': '29.17%'} | [
"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: F4, F8, F1, F7 and F9.",
"Compare and contrast the impact of the following features (F5, F6 and F3) on the model’s prediction of C2.",
"Desc... | [
"F4",
"F8",
"F1",
"F7",
"F9",
"F5",
"F6",
"F3",
"F2"
] | {'F4': 'Checking account', 'F8': 'Duration', 'F1': 'Housing', 'F7': 'Saving accounts', 'F9': 'Sex', 'F5': 'Age', 'F6': 'Purpose', 'F3': 'Job', 'F2': 'Credit amount'} | {'F6': 'F4', 'F8': 'F8', 'F4': 'F1', 'F5': 'F7', 'F2': 'F9', 'F1': 'F5', 'F9': 'F6', 'F3': 'F3', 'F7': 'F2'} | {'C1': 'C2', 'C2': 'C1'} | Good Credit | {'C2': 'Good Credit', 'C1': 'Bad Credit'} |
BernoulliNB | C1 | German Credit Evaluation | The algorithm labels the data given as C1 and the prediction probabilities across the possible labels C1 and C2, respectively, are 51.39% and 48.61%. Judging based on the prediction probabilities, the algorithm shows signs of uncertainty in the above decision. F5, F8, F2, and F1 are the primary contributors to the clas... | [
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"positive",
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] | 341 | 458 | {'C1': '51.39%', 'C2': '48.61%'} | [
"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... | [
"F5",
"F8",
"F2",
"F1",
"F9",
"F7",
"F6",
"F4",
"F3"
] | {'F5': 'Housing', 'F8': 'Checking account', 'F2': 'Sex', 'F1': 'Purpose', 'F9': 'Job', 'F7': 'Duration', 'F6': 'Credit amount', 'F4': 'Age', 'F3': 'Saving accounts'} | {'F4': 'F5', 'F6': 'F8', 'F2': 'F2', 'F9': 'F1', 'F3': 'F9', 'F8': 'F7', 'F7': 'F6', 'F1': 'F4', 'F5': 'F3'} | {'C1': 'C1', 'C2': 'C2'} | Good Credit | {'C1': 'Good Credit', 'C2': 'Bad Credit'} |
GradientBoostingClassifier | C1 | Health Care Services Satisfaction Prediction | The label assignment decision is solely based on the values of the different input features passed to the classification algorithm since the values of these features are used as the basis to make the prediction judgments. The likelihood of any of the classes C1 and C2 being the correct label is 76.26% and 23.74%, respe... | [
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] | 35 | 389 | {'C2': '23.74%', 'C1': '76.26%'} | [
"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 (F6 (value equal to V3), F3 (with a value equal to V3) and F14 (equal to V... | [
"F13",
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"F3",
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"F10",
"F4",
"F7",
"F15",
"F1",
"F5"
] | {'F13': 'Exact diagnosis', 'F9': 'avaliablity of drugs', 'F8': 'lab services', 'F12': 'friendly health care workers', 'F11': 'Communication with dr', 'F6': 'Time waiting', 'F3': 'Specialists avaliable', 'F14': 'Modern equipment', 'F2': 'waiting rooms', 'F16': 'Check up appointment', 'F10': 'Hygiene and cleaning', 'F4':... | {'F9': 'F13', 'F13': 'F9', 'F12': 'F8', 'F11': 'F12', 'F8': 'F11', 'F2': 'F6', 'F7': 'F3', 'F10': 'F14', 'F14': 'F2', 'F1': 'F16', 'F4': 'F10', 'F3': 'F4', 'F5': 'F7', 'F15': 'F15', 'F16': 'F1', 'F6': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Satisfied | {'C2': 'Dissatisfied', 'C1': 'Satisfied'} |
LogisticRegression | C1 | Broadband Sevice Signup | Here the classifier labels the given case as C1 with a moderately high confidence level. Specifically, the prediction likelihood of class C2 is only 21.67%. The main drivers for the classification above are F16, F29, F41, and F27. Among these top features, F16 and F29 have the most significant influence on the classifi... | [
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"neg... | 262 | 172 | {'C2': '21.67%', 'C1': '78.33%'} | [
"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: F16, F29 and F27.",
"Compare and contrast the impact of the following features (F41, F35 and F25) on the model’s prediction of C1.",
"Descri... | [
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"... | {'F16': 'X38', 'F29': 'X32', 'F27': 'X3', 'F41': 'X22', 'F35': 'X16', 'F25': 'X25', 'F14': 'X41', 'F2': 'X35', 'F4': 'X4', 'F19': 'X19', 'F33': 'X12', 'F39': 'X11', 'F24': 'X1', 'F17': 'X10', 'F9': 'X28', 'F26': 'X18', 'F38': 'X21', 'F20': 'X36', 'F1': 'X42', 'F13': 'X27', 'F12': 'X34', 'F37': 'X2', 'F36': 'X37', 'F18'... | {'F35': 'F16', 'F29': 'F29', 'F2': 'F27', 'F20': 'F41', 'F14': 'F35', 'F23': 'F25', 'F39': 'F14', 'F32': 'F2', 'F3': 'F4', 'F17': 'F19', 'F10': 'F33', 'F9': 'F39', 'F40': 'F24', 'F8': 'F17', 'F26': 'F9', 'F16': 'F26', 'F19': 'F38', 'F33': 'F20', 'F38': 'F1', 'F25': 'F13', 'F31': 'F12', 'F1': 'F37', 'F34': 'F36', 'F36':... | {'C2': 'C2', 'C1': 'C1'} | Yes | {'C2': 'No', 'C1': 'Yes'} |
LogisticRegression | C1 | Cab Surge Pricing System | The label assigned in this case by the classifier is C1, with a moderately high prediction confidence of 66.11%. Since the confidence level with respect to this C1 is not 100.0%, it is possible that one of the other labels is the true or correct label, and C2 is the next most likely label. The input variables F7, F8, F... | [
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"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive"
] | 133 | 231 | {'C2': '31.78%', 'C1': '66.11%', 'C3': '2.11%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F7, F8 and F11) on the prediction made for this test case.",
"Compare the direction of impact of the features: F5, F9 and F10.",
"Describe the degree of im... | [
"F7",
"F8",
"F11",
"F5",
"F9",
"F10",
"F12",
"F2",
"F3",
"F6",
"F1",
"F4"
] | {'F7': 'Type_of_Cab', 'F8': 'Trip_Distance', 'F11': 'Destination_Type', 'F5': 'Cancellation_Last_1Month', 'F9': 'Confidence_Life_Style_Index', 'F10': 'Life_Style_Index', 'F12': 'Gender', 'F2': 'Var3', 'F3': 'Customer_Since_Months', 'F6': 'Var1', 'F1': 'Customer_Rating', 'F4': 'Var2'} | {'F2': 'F7', 'F1': 'F8', 'F6': 'F11', 'F8': 'F5', 'F5': 'F9', 'F4': 'F10', 'F12': 'F12', 'F11': 'F2', 'F3': 'F3', 'F9': 'F6', 'F7': 'F1', 'F10': 'F4'} | {'C2': 'C2', 'C3': 'C1', 'C1': 'C3'} | C2 | {'C2': 'Low', 'C1': 'Medium', 'C3': 'High'} |
SVM_poly | C1 | Mobile Price-Range Classification | The classification assertions arrived here are mainly based on the influence and contributions of the different input variables. The prediction probabilities across the four possible classes C2, C3, C4, and C1 are 0.05%, 0.04%, 0.47%, and 99.45%, respectively. Therefore according to the classifier, the most likely cla... | [
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"positive",
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"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative"
] | 448 | 405 | {'C1': '99.45%', 'C4': '0.47%', 'C3': '0.04%', 'C2': '0.05%'} | [
"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... | [
"F12",
"F2",
"F7",
"F8",
"F18",
"F19",
"F13",
"F14",
"F15",
"F16",
"F4",
"F6",
"F1",
"F17",
"F11",
"F10",
"F20",
"F5",
"F3",
"F9"
] | {'F12': 'ram', 'F2': 'battery_power', 'F7': 'px_height', 'F8': 'px_width', 'F18': 'dual_sim', 'F19': 'four_g', 'F13': 'touch_screen', 'F14': 'int_memory', 'F15': 'pc', 'F16': 'n_cores', 'F4': 'fc', 'F6': 'clock_speed', 'F1': 'three_g', 'F17': 'sc_w', 'F11': 'wifi', 'F10': 'm_dep', 'F20': 'mobile_wt', 'F5': 'talk_time',... | {'F11': 'F12', 'F1': 'F2', 'F9': 'F7', 'F10': 'F8', 'F16': 'F18', 'F17': 'F19', 'F19': 'F13', 'F4': 'F14', 'F8': 'F15', 'F7': 'F16', 'F3': 'F4', 'F2': 'F6', 'F18': 'F1', 'F13': 'F17', 'F20': 'F11', 'F5': 'F10', 'F6': 'F20', 'F14': 'F5', 'F12': 'F3', 'F15': 'F9'} | {'C1': 'C1', 'C4': 'C4', 'C2': 'C3', 'C3': 'C2'} | r1 | {'C1': 'r1', 'C4': 'r2', 'C3': 'r3', 'C2': 'r4'} |
BernoulliNB | C1 | Job Change of Data Scientists | The prediction likelihood of class C1 is 84.87%, making it the most probable label for the given case. When making the above prediction, the most relevant features considered are F12, F8, F3, and F9. Conversely, F5, F7, and F10 are the least influential features, with their values receiving little consideration from th... | [
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] | [
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
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] | 248 | 158 | {'C2': '15.13%', 'C1': '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... | [
"F12",
"F9",
"F8",
"F3",
"F2",
"F4",
"F6",
"F10",
"F1",
"F5",
"F7",
"F11"
] | {'F12': 'city', 'F9': 'enrolled_university', 'F8': 'relevent_experience', 'F3': 'city_development_index', 'F2': 'experience', 'F4': 'education_level', 'F6': 'major_discipline', 'F10': 'last_new_job', 'F1': 'gender', 'F5': 'company_size', 'F7': 'company_type', 'F11': 'training_hours'} | {'F3': 'F12', 'F6': 'F9', 'F5': 'F8', 'F1': 'F3', 'F9': 'F2', 'F7': 'F4', 'F8': 'F6', 'F12': 'F10', 'F4': 'F1', 'F10': 'F5', 'F11': 'F7', 'F2': 'F11'} | {'C1': 'C2', 'C2': 'C1'} | Leave | {'C2': 'Stay', 'C1': 'Leave'} |
MLPClassifier | C1 | Annual Income Earnings | With respect to the given case, the most probable label for the given case is C1, with a 99.81% chance of being the correct label, therefore the probability of C2 is only 0.19% for this case. Among the input variables, only four features are shown to have a negative influence on the classification decision above: F3, F... | [
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"0.14",
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"0.08",
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] | [
"positive",
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"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive"
] | 36 | 393 | {'C1': '99.81%', 'C2': '0.19%'} | [
"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... | [
"F1",
"F5",
"F2",
"F8",
"F13",
"F11",
"F3",
"F10",
"F4",
"F6",
"F12",
"F14",
"F9",
"F7"
] | {'F1': 'Capital Gain', 'F5': 'Marital Status', 'F2': 'Capital Loss', 'F8': 'Age', 'F13': 'Hours per week', 'F11': 'Education', 'F3': 'Occupation', 'F10': 'Country', 'F4': 'Relationship', 'F6': 'Workclass', 'F12': 'Sex', 'F14': 'fnlwgt', 'F9': 'Education-Num', 'F7': 'Race'} | {'F11': 'F1', 'F6': 'F5', 'F12': 'F2', 'F1': 'F8', 'F13': 'F13', 'F4': 'F11', 'F7': 'F3', 'F14': 'F10', 'F8': 'F4', 'F2': 'F6', 'F10': 'F12', 'F3': 'F14', 'F5': 'F9', 'F9': 'F7'} | {'C2': 'C1', 'C1': 'C2'} | Under 50K | {'C1': 'Under 50K', 'C2': 'Above 50K'} |
GaussianNB | C2 | Tic-Tac-Toe Strategy | For a particular case, the model predicted the class designation C2 with 75.50% confidence. Based on the attributions analysis, the feature that had the biggest impact on the final labelling decision were the F4 and F2, which happened to strongly support the assignment of label C2. Contributing differently to F4, the f... | [
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] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 86 | 246 | {'C1': '24.50%', 'C2': '75.50%'} | [
"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 (value equal to V2), F9 (value equal to V2), F6 (when it is equal to V... | [
"F4",
"F2",
"F3",
"F9",
"F6",
"F7",
"F5",
"F1",
"F8"
] | {'F4': 'middle-middle-square', 'F2': ' top-right-square', 'F3': 'bottom-middle-square', 'F9': 'middle-right-square', 'F6': 'bottom-left-square', 'F7': 'bottom-right-square', 'F5': 'top-left-square', 'F1': 'middle-left-square', 'F8': 'top-middle-square'} | {'F5': 'F4', 'F3': 'F2', 'F8': 'F3', 'F6': 'F9', 'F7': 'F6', 'F9': 'F7', 'F1': 'F5', 'F4': 'F1', 'F2': 'F8'} | {'C2': 'C1', 'C1': 'C2'} | player B win | {'C1': 'player B lose', 'C2': 'player B win'} |
RandomForestClassifier | C4 | Mobile Price-Range Classification | The model reveals that C2 and C3 each has a zero prediction probability, while C1 has a 3.85%. This indicates that C4 is the most likely label for the present context with approximately 96.15% certainty. F4, F18, and F9 are the most important elements driving the above classification, whereas F16, F7, F6, F14, and F5 a... | [
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"negative",
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"negative"
] | 247 | 349 | {'C2': '0.00%', 'C3': '0.00%', 'C1': '3.85%', 'C4': '96.15%'} | [
"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... | [
"F18",
"F4",
"F9",
"F10",
"F8",
"F12",
"F13",
"F2",
"F11",
"F17",
"F3",
"F19",
"F15",
"F1",
"F20",
"F16",
"F7",
"F6",
"F14",
"F5"
] | {'F18': 'ram', 'F4': 'battery_power', 'F9': 'px_width', 'F10': 'int_memory', 'F8': 'pc', 'F12': 'touch_screen', 'F13': 'four_g', 'F2': 'm_dep', 'F11': 'px_height', 'F17': 'clock_speed', 'F3': 'sc_h', 'F19': 'n_cores', 'F15': 'talk_time', 'F1': 'blue', 'F20': 'dual_sim', 'F16': 'fc', 'F7': 'mobile_wt', 'F6': 'sc_w', 'F1... | {'F11': 'F18', 'F1': 'F4', 'F10': 'F9', 'F4': 'F10', 'F8': 'F8', 'F19': 'F12', 'F17': 'F13', 'F5': 'F2', 'F9': 'F11', 'F2': 'F17', 'F12': 'F3', 'F7': 'F19', 'F14': 'F15', 'F15': 'F1', 'F16': 'F20', 'F3': 'F16', 'F6': 'F7', 'F13': 'F6', 'F20': 'F14', 'F18': 'F5'} | {'C4': 'C2', 'C3': 'C3', 'C1': 'C1', 'C2': 'C4'} | r4 | {'C2': 'r1', 'C3': 'r2', 'C1': 'r3', 'C4': 'r4'} |
LogisticRegression | C2 | House Price Classification | The prediction is that class label C2 is very likely the correct label, given that the associated confidence level is 99.93%. The features F11, F2, and F8 appear to have very smaller or little impact on the prediction of C2 compared to F5, F7, F13, F1, and F10, according to the attribution analysis. F5 and F7 are the f... | [
"0.35",
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"0.21",
"0.18",
"-0.16",
"0.07",
"0.07",
"0.06",
"-0.04",
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"-0.02",
"0.01",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive"
] | 67 | 22 | {'C1': '0.07%', 'C2': '99.93%'} | [
"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, F12 and F6) on the model’s prediction of C2.",
"Summarize the set of ... | [
"F5",
"F7",
"F13",
"F1",
"F10",
"F12",
"F6",
"F3",
"F4",
"F9",
"F8",
"F2",
"F11"
] | {'F5': 'LSTAT', 'F7': 'RM', 'F13': 'PTRATIO', 'F1': 'RAD', 'F10': 'CHAS', 'F12': 'TAX', 'F6': 'CRIM', 'F3': 'DIS', 'F4': 'AGE', 'F9': 'B', 'F8': 'ZN', 'F2': 'NOX', 'F11': 'INDUS'} | {'F13': 'F5', 'F6': 'F7', 'F11': 'F13', 'F9': 'F1', 'F4': 'F10', 'F10': 'F12', 'F1': 'F6', 'F8': 'F3', 'F7': 'F4', 'F12': 'F9', 'F2': 'F8', 'F5': 'F2', 'F3': 'F11'} | {'C1': 'C1', 'C2': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
GaussianNB | C2 | Tic-Tac-Toe Strategy | For the given case, the model predicted the class label C2 with a certainty of around 75.50%. By far, the feature with the most impact on the final classification was F4, which positively supports the decision. Feature F9 was the feature that contributed the most to pushing away the classification decision from C2, tha... | [
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"0.10",
"0.10",
"-0.09",
"-0.09",
"-0.07",
"-0.07",
"-0.03"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 86 | 34 | {'C1': '24.50%', 'C2': '75.50%'} | [
"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 (value equal to V2), F2 (value equal to V2), F9 (when it is equal to V... | [
"F4",
"F1",
"F8",
"F2",
"F9",
"F6",
"F7",
"F5",
"F3"
] | {'F4': 'middle-middle-square', 'F1': ' top-right-square', 'F8': 'bottom-middle-square', 'F2': 'middle-right-square', 'F9': 'bottom-left-square', 'F6': 'bottom-right-square', 'F7': 'top-left-square', 'F5': 'middle-left-square', 'F3': 'top-middle-square'} | {'F5': 'F4', 'F3': 'F1', 'F8': 'F8', 'F6': 'F2', 'F7': 'F9', 'F9': 'F6', 'F1': 'F7', 'F4': 'F5', 'F2': 'F3'} | {'C1': 'C1', 'C2': 'C2'} | player B win | {'C1': 'player B lose', 'C2': 'player B win'} |
LogisticRegression | C2 | Food Ordering Customer Churn Prediction | Judging based on the values of the input features, a decision is made by the classifier to label the given data as C2 with a prediction confidence equal to 84.90%. The major influential features resulting in the classification here are F40, F23, F10, and F37. F40 and F23 are identified as the most negative features, wi... | [
"-0.20",
"-0.12",
"0.11",
"0.11",
"-0.09",
"-0.09",
"-0.09",
"0.08",
"0.08",
"0.07",
"0.07",
"-0.06",
"-0.06",
"0.06",
"0.06",
"-0.06",
"0.06",
"-0.05",
"0.05",
"0.05",
"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",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negligible",
"negligible",
"neg... | 271 | 178 | {'C2': '84.90%', 'C1': '15.10%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Summarize the direction of influence of the variables (F40, F23 and F37) on the prediction made for this test case.",
"Compare the direction of impact of the variables: F10, F15 a... | [
"F40",
"F23",
"F37",
"F10",
"F15",
"F35",
"F27",
"F41",
"F44",
"F6",
"F3",
"F29",
"F5",
"F4",
"F1",
"F17",
"F43",
"F34",
"F33",
"F18",
"F25",
"F30",
"F45",
"F28",
"F7",
"F19",
"F11",
"F39",
"F31",
"F42",
"F21",
"F46",
"F20",
"F9",
"F8",
"F13",
... | {'F40': 'Unaffordable', 'F23': 'Late Delivery', 'F37': 'Good Food quality', 'F10': 'Perference(P2)', 'F15': 'Delay of delivery person picking up food', 'F35': 'Influence of rating', 'F27': 'Wrong order delivered', 'F41': 'Time saving', 'F44': 'Ease and convenient', 'F6': 'Order Time', 'F3': 'Google Maps Accuracy', 'F29... | {'F23': 'F40', 'F19': 'F23', 'F15': 'F37', 'F9': 'F10', 'F26': 'F15', 'F38': 'F35', 'F27': 'F27', 'F11': 'F41', 'F10': 'F44', 'F31': 'F6', 'F34': 'F3', 'F43': 'F29', 'F42': 'F5', 'F24': 'F4', 'F35': 'F1', 'F40': 'F17', 'F5': 'F43', 'F28': 'F34', 'F14': 'F33', 'F22': 'F18', 'F30': 'F25', 'F37': 'F30', 'F36': 'F45', 'F39... | {'C2': 'C2', 'C1': 'C1'} | Return | {'C2': 'Return', 'C1': 'Go Away'} |
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