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 0 468 | 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 stringclasses 25
values | predicted_class_label stringlengths 2 23 | class2name stringlengths 25 85 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GradientBoostingClassifier | C2 | Food Ordering Customer Churn Prediction | The prediction probability of C1 is 17.93% and that of C2 is 82.07%. Therefore, the most probable class for the given case is C2. The above classification assertion statements are based on the information supplied to the classifier about the case given. The top features with significant attributions leading to the deci... | [
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"neg... | 7 | 362 | {'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 (F6 (when it is equal to V1), F26 (value equal to V1), F32 (equal to V0), F42 (when it is equal to V1) and F25 (when it is equal to V3)) on the prediction ... | [
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... | {'F6': 'More restaurant choices', 'F26': 'Ease and convenient', 'F32': 'Bad past experience', 'F42': 'Time saving', 'F25': 'Unaffordable', 'F43': 'Educational Qualifications', 'F29': 'Late Delivery', 'F7': 'Occupation', 'F39': 'Influence of rating', 'F5': 'Less Delivery time', 'F13': 'Order placed by mistake', 'F41': '... | {'F12': 'F6', 'F10': 'F26', 'F21': 'F32', 'F11': 'F42', 'F23': 'F25', 'F6': 'F43', 'F19': 'F29', 'F4': 'F7', 'F38': 'F39', 'F39': 'F5', 'F29': 'F13', 'F37': 'F41', 'F31': 'F12', 'F22': 'F4', 'F14': 'F20', 'F26': 'F35', 'F45': 'F16', 'F27': 'F21', 'F43': 'F10', 'F28': 'F1', 'F33': 'F38', 'F34': 'F14', 'F1': 'F24', 'F35'... | {'C1': 'C1', 'C2': 'C2'} | Go Away | {'C1': 'Return', 'C2': 'Go Away'} |
DNN | C1 | Credit Card Fraud Classification | The classification algorithm classifies the given case as C1 with a confidence level equal to 99.99%, suggesting that there is little chance that the C2 label could be the true label. The classification confidence level can be attributed to the influence and contributions of the features F29, F8, F10, F26, and F23. Pos... | [
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"positiv... | 129 | 431 | {'C1': '99.99%', 'C2': '0.01%'} | [
"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: F29 and F8.",
"Compare and contrast the impact of the following features (F10, F23, F26 and F4) on the model’s prediction of C1.",
"Describe... | [
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] | {'F29': 'Z3', 'F8': 'Z6', 'F10': 'Time', 'F23': 'Z13', 'F26': 'Z12', 'F4': 'Z4', 'F7': 'Z10', 'F25': 'Z5', 'F3': 'Z9', 'F24': 'Z14', 'F27': 'Z16', 'F30': 'Z11', 'F12': 'Z17', 'F21': 'Z19', 'F16': 'Z8', 'F9': 'Z28', 'F28': 'Z21', 'F20': 'Z20', 'F13': 'Z1', 'F22': 'Z24', 'F6': 'Z18', 'F5': 'Z2', 'F14': 'Z25', 'F1': 'Amou... | {'F4': 'F29', 'F7': 'F8', 'F1': 'F10', 'F14': 'F23', 'F13': 'F26', 'F5': 'F4', 'F11': 'F7', 'F6': 'F25', 'F10': 'F3', 'F15': 'F24', 'F17': 'F27', 'F12': 'F30', 'F18': 'F12', 'F20': 'F21', 'F9': 'F16', 'F29': 'F9', 'F22': 'F28', 'F21': 'F20', 'F2': 'F13', 'F25': 'F22', 'F19': 'F6', 'F3': 'F5', 'F26': 'F14', 'F30': 'F1',... | {'C1': 'C1', 'C2': 'C2'} | Not Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
RandomForestClassifier | C2 | Employee Attrition | The data is marked as C2 by the classifier based on the input features, with a moderate degree of confidence since the prediction probability of the other label, C1, is only 44.0%. The most influential features driving the classification above are F27, F12, F24, F14, F30, F18, F26, F2, F20, F22, F13, F21, F17, F23, F10... | [
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"neg... | 27 | 382 | {'C1': '44.00%', 'C2': '56.00%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F24 (value equal to V2), F14 (value equal to V1), F30 (with a value equal ... | [
"F27",
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] | {'F27': 'OverTime', 'F12': 'BusinessTravel', 'F24': 'MaritalStatus', 'F14': 'JobInvolvement', 'F30': 'WorkLifeBalance', 'F18': 'Education', 'F26': 'EnvironmentSatisfaction', 'F2': 'Gender', 'F20': 'JobRole', 'F22': 'NumCompaniesWorked', 'F13': 'YearsInCurrentRole', 'F21': 'HourlyRate', 'F17': 'Department', 'F23': 'Rela... | {'F26': 'F27', 'F17': 'F12', 'F25': 'F24', 'F29': 'F14', 'F20': 'F30', 'F27': 'F18', 'F28': 'F26', 'F23': 'F2', 'F24': 'F20', 'F8': 'F22', 'F14': 'F13', 'F4': 'F21', 'F21': 'F17', 'F18': 'F23', 'F19': 'F10', 'F16': 'F28', 'F1': 'F25', 'F7': 'F5', 'F10': 'F6', 'F30': 'F8', 'F2': 'F15', 'F15': 'F7', 'F13': 'F9', 'F12': '... | {'C1': 'C1', 'C2': 'C2'} | Leave | {'C1': 'Leave', 'C2': 'Leave'} |
RandomForestClassifier | C1 | Cab Surge Pricing System | The model determined that this case belongs to C1 of the three possible labels, with an 83.0% likelihood. It is important to note, however, that there is about a 14.0% chance that it could be C2 and a 3.0% chance that it is rather C3. The most relevant feature driving this prediction is F12, with a very strong positive... | [
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] | [
"positive",
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"positive",
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"positive",
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] | 124 | 57 | {'C3': '3.00%', 'C1': '83.00%', 'C2': '14.00%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F4, F7, F5 (when it is equal to V2) and F2) on the model’s prediction of C1... | [
"F12",
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"F7",
"F5",
"F2",
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] | {'F12': 'Type_of_Cab', 'F11': 'Destination_Type', 'F4': 'Trip_Distance', 'F7': 'Cancellation_Last_1Month', 'F5': 'Confidence_Life_Style_Index', 'F2': 'Var3', 'F6': 'Customer_Since_Months', 'F9': 'Life_Style_Index', 'F1': 'Var2', 'F3': 'Gender', 'F10': 'Var1', 'F8': 'Customer_Rating'} | {'F2': 'F12', 'F6': 'F11', 'F1': 'F4', 'F8': 'F7', 'F5': 'F5', 'F11': 'F2', 'F3': 'F6', 'F4': 'F9', 'F10': 'F1', 'F12': 'F3', 'F9': 'F10', 'F7': 'F8'} | {'C1': 'C3', 'C2': 'C1', 'C3': 'C2'} | C2 | {'C3': 'Low', 'C1': 'Medium', 'C2': 'High'} |
RandomForestClassifier | C1 | Annual Income Earnings | The classifier assigned the label C1, given that there is merely a 2.18% chance that C2 is the correct label. Influencing this classification decision are mainly the values of the variables F5, F12, F11, and F4 which are also commonly referred to as positive variables since they increase the response in favour of the p... | [
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] | [
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"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive"
] | 164 | 90 | {'C1': '97.82%', 'C2': '2.18%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F4, F1 and F14) on the model’s prediction of C1.",
"Summarize the set of f... | [
"F5",
"F12",
"F11",
"F4",
"F1",
"F14",
"F3",
"F6",
"F2",
"F8",
"F13",
"F9",
"F7",
"F10"
] | {'F5': 'Capital Gain', 'F12': 'Marital Status', 'F11': 'Relationship', 'F4': 'Age', 'F1': 'Education-Num', 'F14': 'Hours per week', 'F3': 'Occupation', 'F6': 'Capital Loss', 'F2': 'Sex', 'F8': 'Education', 'F13': 'Race', 'F9': 'fnlwgt', 'F7': 'Country', 'F10': 'Workclass'} | {'F11': 'F5', 'F6': 'F12', 'F8': 'F11', 'F1': 'F4', 'F5': 'F1', 'F13': 'F14', 'F7': 'F3', 'F12': 'F6', 'F10': 'F2', 'F4': 'F8', 'F9': 'F13', 'F3': 'F9', 'F14': 'F7', 'F2': 'F10'} | {'C1': 'C1', 'C2': 'C2'} | Under 50K | {'C1': 'Under 50K', 'C2': 'Above 50K'} |
SVMClassifier_poly | C1 | Employee Attrition | Because the chance that C2 is the right label is around 42.17 percent, the example under review is labelled as C1 with a moderate degree of confidence. F4, F1, F7, F15, F6, and F14 have the most influence on the above forecast, whereas F25, F21, F11, F13, F29, F10, and F26 have small contributions. F20, F22, F5, F8, F1... | [
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"neg... | 428 | 353 | {'C1': '57.83%', 'C2': '42.17%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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] | {'F4': 'OverTime', 'F1': 'NumCompaniesWorked', 'F7': 'RelationshipSatisfaction', 'F15': 'MaritalStatus', 'F6': 'YearsSinceLastPromotion', 'F14': 'Department', 'F25': 'Age', 'F21': 'Education', 'F11': 'EducationField', 'F13': 'BusinessTravel', 'F29': 'JobLevel', 'F10': 'JobInvolvement', 'F26': 'WorkLifeBalance', 'F20': ... | {'F26': 'F4', 'F8': 'F1', 'F18': 'F7', 'F25': 'F15', 'F15': 'F6', 'F21': 'F14', 'F1': 'F25', 'F27': 'F21', 'F22': 'F11', 'F17': 'F13', 'F5': 'F29', 'F29': 'F10', 'F20': 'F26', 'F7': 'F20', 'F13': 'F22', 'F23': 'F5', 'F19': 'F8', 'F24': 'F17', 'F12': 'F12', 'F28': 'F24', 'F16': 'F9', 'F2': 'F2', 'F14': 'F16', 'F11': 'F2... | {'C1': 'C1', 'C2': 'C2'} | Leave | {'C1': 'Leave', 'C2': 'Leave'} |
BernoulliNB | C1 | Credit Card Fraud Classification | All features are shown to have a positive impact on the classification to class C1 or to have no impact at all. F12, F1, F17, and F28 are the four features with the most impact. Some of the remaining features, in order of feature importance, are F5, F7, F10, F27, F21, F18, F2, F23, F24, F29, F11, and F6. F12 and F1 bo... | [
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"neg... | 85 | 33 | {'C2': '5.16%', 'C1': '94.84%'} | [
"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 F1) on the prediction made for this test case.",
"Compare the direction of impact of the features: F17, F28, F5 and F7.",
"Describe the degree of i... | [
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] | {'F12': 'Z14', 'F1': 'Z1', 'F17': 'Z17', 'F28': 'Amount', 'F5': 'Z19', 'F7': 'Z5', 'F10': 'Z3', 'F27': 'Z8', 'F21': 'Z18', 'F18': 'Z10', 'F2': 'Z26', 'F23': 'Z25', 'F24': 'Z22', 'F29': 'Z4', 'F11': 'Z7', 'F6': 'Z13', 'F26': 'Z23', 'F19': 'Z9', 'F8': 'Z21', 'F4': 'Z2', 'F22': 'Z28', 'F30': 'Z24', 'F25': 'Z27', 'F14': 'T... | {'F15': 'F12', 'F2': 'F1', 'F18': 'F17', 'F30': 'F28', 'F20': 'F5', 'F6': 'F7', 'F4': 'F10', 'F9': 'F27', 'F19': 'F21', 'F11': 'F18', 'F27': 'F2', 'F26': 'F23', 'F23': 'F24', 'F5': 'F29', 'F8': 'F11', 'F14': 'F6', 'F24': 'F26', 'F10': 'F19', 'F22': 'F8', 'F3': 'F4', 'F29': 'F22', 'F25': 'F30', 'F28': 'F25', 'F1': 'F14'... | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
LogisticRegression | C1 | Used Cars Price-Range Prediction | Label C2 has a lower probability than label C1, so C1 is the most likely option in this case. C1 has a probability of approximately 96.25 percent, which can be attributed to variables such as F7, F2, F8, and F6. According to the attributions assessment, the least relevant variables are F3, F4, and F9. Inspection of the... | [
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] | 412 | 355 | {'C1': '96.25%', 'C2': '3.75%'} | [
"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",
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"F10",
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] | {'F2': 'Fuel_Type', 'F7': 'Power', 'F8': 'Engine', 'F6': 'Seats', 'F10': 'car_age', 'F1': 'Owner_Type', 'F5': 'Name', 'F3': 'Mileage', 'F4': 'Kilometers_Driven', 'F9': 'Transmission'} | {'F7': 'F2', 'F4': 'F7', 'F3': 'F8', 'F10': 'F6', 'F5': 'F10', 'F9': 'F1', 'F6': 'F5', 'F2': 'F3', 'F1': 'F4', 'F8': 'F9'} | {'C1': 'C1', 'C2': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
GradientBoostingClassifier | C2 | Paris House Classification | According to the prediction made here, the most likely label for the given case is C2, with a prediction probability of 97.02%, indicating that the prediction probability of C1 is only 2.98%. The classification above is mainly due to the influence of F9, F13, and F5. The next set of features with moderate contributions... | [
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] | 255 | 165 | {'C1': '2.98%', 'C2': '97.02%'} | [
"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... | [
"F9",
"F13",
"F5",
"F1",
"F12",
"F6",
"F3",
"F16",
"F14",
"F17",
"F2",
"F11",
"F8",
"F7",
"F4",
"F10",
"F15"
] | {'F9': 'isNewBuilt', 'F13': 'hasYard', 'F5': 'hasPool', 'F1': 'hasStormProtector', 'F12': 'made', 'F6': 'squareMeters', 'F3': 'floors', 'F16': 'cityCode', 'F14': 'hasGuestRoom', 'F17': 'basement', 'F2': 'numPrevOwners', 'F11': 'price', 'F8': 'numberOfRooms', 'F7': 'garage', 'F4': 'cityPartRange', 'F10': 'hasStorageRoom... | {'F3': 'F9', 'F1': 'F13', 'F2': 'F5', 'F4': 'F1', 'F12': 'F12', 'F6': 'F6', 'F8': 'F3', 'F9': 'F16', 'F16': 'F14', 'F13': 'F17', 'F11': 'F2', 'F17': 'F11', 'F7': 'F8', 'F15': 'F7', 'F10': 'F4', 'F5': 'F10', 'F14': 'F15'} | {'C1': 'C1', 'C2': 'C2'} | Luxury | {'C1': 'Basic', 'C2': 'Luxury'} |
RandomForestClassifier | C1 | Flight Price-Range Classification | The classification conclusion is as follows: C1 is the most likely label for this case and the classifier is certain that neither C3 nor C2 are the right labels since their likelihoods are equal to zero. The driving factors for the above classification are F6, F11, and F12, all of which have a substantial positive impa... | [
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"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative"
] | 250 | 343 | {'C1': '100.00%', 'C3': '0.00%', 'C2': '0.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F6",
"F11",
"F12",
"F1",
"F10",
"F4",
"F2",
"F8",
"F3",
"F9",
"F5",
"F7"
] | {'F6': 'Airline', 'F11': 'Duration_hours', 'F12': 'Total_Stops', 'F1': 'Journey_month', 'F10': 'Source', 'F4': 'Destination', 'F2': 'Arrival_hour', 'F8': 'Journey_day', 'F3': 'Dep_minute', 'F9': 'Arrival_minute', 'F5': 'Duration_mins', 'F7': 'Dep_hour'} | {'F9': 'F6', 'F7': 'F11', 'F12': 'F12', 'F2': 'F1', 'F10': 'F10', 'F11': 'F4', 'F5': 'F2', 'F1': 'F8', 'F4': 'F3', 'F6': 'F9', 'F8': 'F5', 'F3': 'F7'} | {'C1': 'C1', 'C2': 'C3', 'C3': 'C2'} | Low | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
KNeighborsClassifier | C2 | Water Quality Classification | The classifier states that there is a 50.0% chance that the true label of this test observation is C2. This indicates that the classifier is less certain in its prediction decision regarding the case under consideration. The label assigned is mainly due to the values of the features F9, F6, F4, F1, F2, and F7. The top ... | [
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] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive"
] | 94 | 40 | {'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... | [
"F9",
"F6",
"F4",
"F1",
"F2",
"F7",
"F5",
"F8",
"F3"
] | {'F9': 'Hardness', 'F6': 'Sulfate', 'F4': 'Organic_carbon', 'F1': 'Solids', 'F2': 'Conductivity', 'F7': 'Trihalomethanes', 'F5': 'ph', 'F8': 'Turbidity', 'F3': 'Chloramines'} | {'F2': 'F9', 'F5': 'F6', 'F7': 'F4', 'F3': 'F1', 'F6': 'F2', 'F8': 'F7', 'F1': 'F5', 'F9': 'F8', 'F4': 'F3'} | {'C1': 'C2', 'C2': 'C1'} | Not Portable | {'C2': 'Not Portable', 'C1': 'Portable'} |
GradientBoostingClassifier | C2 | Printer Sales | According to the attribution analysis, the each input variables contributes differently to the decision. For the case under consideration, there are variables that have negative influence on the decision here, but it also has numerous quantifiable variables that are positive. Per the model, C2 is 91.95% certain to be t... | [
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"neg... | 111 | 240 | {'C1': '8.05%', 'C2': '91.95%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F17, F26 and F15) on the model’s prediction of C2.",
"Summarize the set of... | [
"F1",
"F7",
"F2",
"F8",
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"F21",
"F12",
"F11",
"F16",
"F14",
"F18",
"F9",
"F20",
"F5",
"F22"
] | {'F1': 'X24', 'F7': 'X8', 'F2': 'X1', 'F8': 'X21', 'F3': 'X4', 'F17': 'X6', 'F26': 'X3', 'F15': 'X22', 'F4': 'X7', 'F19': 'X15', 'F6': 'X20', 'F24': 'X11', 'F25': 'X10', 'F13': 'X19', 'F10': 'X5', 'F23': 'X16', 'F21': 'X23', 'F12': 'X9', 'F11': 'X17', 'F16': 'X18', 'F14': 'X25', 'F18': 'X14', 'F9': 'X2', 'F20': 'X13', ... | {'F24': 'F1', 'F8': 'F7', 'F1': 'F2', 'F21': 'F8', 'F4': 'F3', 'F6': 'F17', 'F3': 'F26', 'F22': 'F15', 'F7': 'F4', 'F15': 'F19', 'F20': 'F6', 'F11': 'F24', 'F10': 'F25', 'F19': 'F13', 'F5': 'F10', 'F16': 'F23', 'F23': 'F21', 'F9': 'F12', 'F17': 'F11', 'F18': 'F16', 'F25': 'F14', 'F14': 'F18', 'F2': 'F9', 'F13': 'F20', ... | {'C1': 'C1', 'C2': 'C2'} | More | {'C1': 'Less', 'C2': 'More'} |
LogisticRegression | C2 | Annual Income Earnings | Deciding the most probable label for the given case on the basis of the values of the input variables, the classification algorithm's output decision is that: the probability of C2 being the correct label is 79.78%, the probability of C1 is 20.22%. Therefore, the most likely label is identified as C2 and the attributi... | [
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"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
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"positive",
"negative"
] | 40 | 394 | {'C1': '20.22%', 'C2': '79.78%'} | [
"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, F8 (equal to V2), F14 (when it is equal to V12), F7 and F4) on the prediction made for this test case.",
"Compare the direction of impact of the featur... | [
"F9",
"F8",
"F14",
"F7",
"F4",
"F12",
"F2",
"F1",
"F10",
"F3",
"F5",
"F13",
"F6",
"F11"
] | {'F9': 'Capital Gain', 'F8': 'Marital Status', 'F14': 'Education', 'F7': 'Capital Loss', 'F4': 'Hours per week', 'F12': 'Sex', 'F2': 'Country', 'F1': 'Education-Num', 'F10': 'Occupation', 'F3': 'Race', 'F5': 'Age', 'F13': 'Workclass', 'F6': 'fnlwgt', 'F11': 'Relationship'} | {'F11': 'F9', 'F6': 'F8', 'F4': 'F14', 'F12': 'F7', 'F13': 'F4', 'F10': 'F12', 'F14': 'F2', 'F5': 'F1', 'F7': 'F10', 'F9': 'F3', 'F1': 'F5', 'F2': 'F13', 'F3': 'F6', 'F8': 'F11'} | {'C1': 'C1', 'C2': 'C2'} | Above 50K | {'C1': 'Under 50K', 'C2': 'Above 50K'} |
LogisticRegression | C2 | E-Commerce Shipping | The reliability of the classification verdict for this case is 71.57%, implying there is a 28.43% chance that the correct label could be C1. F6 has a significant negative impact on classification output since its contribution contradicts the labelling of the case as C2, hence favours labelling the case as C1. The value... | [
"-0.25",
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"0.04",
"0.02",
"0.01",
"0.01",
"0.01",
"0.00",
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] | [
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 70 | 251 | {'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 (F8 (with a value equal to V4), F7 (when it is equal to V2), F1 and F4 (whe... | [
"F6",
"F5",
"F8",
"F7",
"F1",
"F4",
"F2",
"F3",
"F10",
"F9"
] | {'F6': 'Discount_offered', 'F5': 'Weight_in_gms', 'F8': 'Prior_purchases', 'F7': 'Product_importance', 'F1': 'Cost_of_the_Product', 'F4': 'Gender', 'F2': 'Customer_rating', 'F3': 'Warehouse_block', 'F10': 'Customer_care_calls', 'F9': 'Mode_of_Shipment'} | {'F2': 'F6', 'F3': 'F5', 'F8': 'F8', 'F9': 'F7', 'F1': 'F1', 'F10': 'F4', 'F7': 'F2', 'F4': 'F3', 'F6': 'F10', 'F5': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | On-time | {'C2': 'On-time', 'C1': 'Late'} |
DecisionTreeClassifier | C1 | Vehicle Insurance Claims | This model predicted class label C1 with about 93.32% certainty, while there was about a 6.68% chance of the correct class being identified as a different label. Seven features, F30, F5, F33, F10, F11, F9, and F6, have higher impacts on the model prediction decision above. But the feature F30 has the largest positive i... | [
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"negative",
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"negligible",
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"neg... | 74 | 26 | {'C1': '93.32%', 'C2': '6.68%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F30",
"F5",
"F33",
"F10",
"F11",
"F9",
"F6",
"F25",
"F12",
"F15",
"F27",
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"F28",
"F29",
"F26",
"F13",
"F2",
"F16",
"F17",
"F3",
"F8",
"F21"
] | {'F30': 'incident_severity', 'F5': 'incident_city', 'F33': 'injury_claim', 'F10': 'insured_occupation', 'F11': 'insured_zip', 'F9': 'authorities_contacted', 'F6': 'auto_year', 'F25': 'police_report_available', 'F12': 'bodily_injuries', 'F15': 'insured_hobbies', 'F27': 'insured_sex', 'F1': 'auto_make', 'F24': 'property_... | {'F27': 'F30', 'F30': 'F5', 'F14': 'F33', 'F22': 'F10', 'F6': 'F11', 'F28': 'F9', 'F17': 'F6', 'F32': 'F25', 'F11': 'F12', 'F23': 'F15', 'F20': 'F27', 'F33': 'F1', 'F31': 'F24', 'F12': 'F31', 'F24': 'F22', 'F2': 'F7', 'F16': 'F32', 'F1': 'F4', 'F15': 'F20', 'F25': 'F18', 'F7': 'F23', 'F3': 'F14', 'F4': 'F19', 'F29': 'F... | {'C1': 'C1', 'C2': 'C2'} | Not Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
KNeighborsClassifier | C2 | Printer Sales | Considering the prediction likelihoods, this case is labelled as C2 by the model, that is, the model states that there is about an 83.33% chance that the case is under C2 and about a 16.67% chance that it is not. The most relevant features influencing the decision made here are: F20, F7, F17, and F5. Among the feature ... | [
"0.17",
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] | [
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"negative",
"positive",
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"negative",
"positive",
"positive",
"positive",
"negative",
"negligible",
"negligible",
"neg... | 122 | 55 | {'C2': '83.33%', 'C1': '16.67%'} | [
"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... | [
"F20",
"F7",
"F5",
"F17",
"F8",
"F24",
"F21",
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"F16",
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"F19",
"F9",
"F12",
"F23",
"F6",
"F4",
"F14",
"F22",
"F13",
"F3"
] | {'F20': 'X24', 'F7': 'X1', 'F5': 'X4', 'F17': 'X10', 'F8': 'X2', 'F24': 'X8', 'F21': 'X17', 'F25': 'X7', 'F16': 'X21', 'F2': 'X18', 'F11': 'X6', 'F26': 'X11', 'F10': 'X22', 'F15': 'X25', 'F18': 'X5', 'F1': 'X19', 'F19': 'X15', 'F9': 'X23', 'F12': 'X16', 'F23': 'X3', 'F6': 'X14', 'F4': 'X20', 'F14': 'X13', 'F22': 'X12',... | {'F24': 'F20', 'F1': 'F7', 'F4': 'F5', 'F10': 'F17', 'F2': 'F8', 'F8': 'F24', 'F17': 'F21', 'F7': 'F25', 'F21': 'F16', 'F18': 'F2', 'F6': 'F11', 'F11': 'F26', 'F22': 'F10', 'F25': 'F15', 'F5': 'F18', 'F19': 'F1', 'F15': 'F19', 'F23': 'F9', 'F16': 'F12', 'F3': 'F23', 'F14': 'F6', 'F20': 'F4', 'F13': 'F14', 'F12': 'F22',... | {'C1': 'C2', 'C2': 'C1'} | Less | {'C2': 'Less', 'C1': 'More'} |
SVC | C1 | Advertisement Prediction | For the given instance, the model generated the label C1 with a very high predicted probability equal to 99.66% which implies that the model is very confident that C2 is not the correct label. Ranking the contributions of the features to the prediction above, from the most relevant to the least relevant, is as follows:... | [
"0.41",
"0.39",
"0.16",
"0.02",
"-0.02",
"-0.01",
"0.01"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive"
] | 193 | 112 | {'C2': '0.34%', 'C1': '99.66%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F2, F5, F1, F3 and F4.",
"Su... | [
"F2",
"F5",
"F1",
"F3",
"F4",
"F6",
"F7"
] | {'F2': 'Daily Time Spent on Site', 'F5': 'Daily Internet Usage', 'F1': 'Age', 'F3': 'Gender', 'F4': 'ad_day', 'F6': 'ad_month', 'F7': 'Area Income'} | {'F1': 'F2', 'F4': 'F5', 'F2': 'F1', 'F5': 'F3', 'F7': 'F4', 'F6': 'F6', 'F3': 'F7'} | {'C1': 'C2', 'C2': 'C1'} | Watch | {'C2': 'Skip', 'C1': 'Watch'} |
LogisticRegression | C1 | Student Job Placement | The final prediction given by the model was C1 with almost 100% certainty, showing the model is confident about its decision. F10 had significantly more influence on the prediction than any other feature with F3 and F5 having the next highest attribution values. All the top features, F10, F3, and F5, encouraged the mod... | [
"0.42",
"0.20",
"0.17",
"0.13",
"-0.11",
"-0.09",
"0.08",
"-0.07",
"0.05",
"-0.04",
"0.03",
"0.02"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive"
] | 87 | 35 | {'C1': '98.47%', 'C2': '1.53%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the predic... | [
"F10",
"F3",
"F5",
"F7",
"F4",
"F6",
"F1",
"F8",
"F2",
"F11",
"F12",
"F9"
] | {'F10': 'ssc_p', 'F3': 'hsc_p', 'F5': 'degree_p', 'F7': 'gender', 'F4': 'degree_t', 'F6': 'workex', 'F1': 'specialisation', 'F8': 'etest_p', 'F2': 'hsc_s', 'F11': 'hsc_b', 'F12': 'ssc_b', 'F9': 'mba_p'} | {'F1': 'F10', 'F2': 'F3', 'F3': 'F5', 'F6': 'F7', 'F10': 'F4', 'F11': 'F6', 'F12': 'F1', 'F4': 'F8', 'F9': 'F2', 'F8': 'F11', 'F7': 'F12', 'F5': 'F9'} | {'C1': 'C1', 'C2': 'C2'} | Not Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
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 F10, F8, and F7 have lower con... | [
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"negative",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive"
] | 35 | 391 | {'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 (F11 (value equal to V3), F9 (with a value equal to V3) and F4 (equal to V... | [
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"F8",
"F7"
] | {'F16': 'Exact diagnosis', 'F15': 'avaliablity of drugs', 'F5': 'lab services', 'F3': 'friendly health care workers', 'F12': 'Communication with dr', 'F11': 'Time waiting', 'F9': 'Specialists avaliable', 'F4': 'Modern equipment', 'F6': 'waiting rooms', 'F1': 'Check up appointment', 'F13': 'Hygiene and cleaning', 'F2': ... | {'F9': 'F16', 'F13': 'F15', 'F12': 'F5', 'F11': 'F3', 'F8': 'F12', 'F2': 'F11', 'F7': 'F9', 'F10': 'F4', 'F14': 'F6', 'F1': 'F1', 'F4': 'F13', 'F3': 'F2', 'F5': 'F14', 'F15': 'F10', 'F16': 'F8', 'F6': 'F7'} | {'C1': 'C2', 'C2': 'C1'} | Satisfied | {'C2': 'Dissatisfied', 'C1': 'Satisfied'} |
SGDClassifier | C2 | Flight Price-Range Classification | According to the model, C3 is the least probable class, while the most probable class for the given case is identified as C2. The top two variables with the greatest control over the model in terms of this case's label assignment are F11 and F4 but on the contrary, the rest of the variables have moderate-to-lower influ... | [
"0.33",
"-0.22",
"0.09",
"0.04",
"-0.03",
"0.03",
"0.03",
"-0.02",
"0.02",
"-0.02",
"0.02",
"-0.02"
] | [
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 50 | 260 | {'C2': '86.54%', 'C1': '13.46%', '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: F11 (equal to V8), F4 (with a... | [
"F11",
"F4",
"F2",
"F1",
"F9",
"F7",
"F6",
"F3",
"F8",
"F12",
"F5",
"F10"
] | {'F11': 'Airline', 'F4': 'Total_Stops', 'F2': 'Source', 'F1': 'Journey_month', 'F9': 'Arrival_minute', 'F7': 'Journey_day', 'F6': 'Duration_hours', 'F3': 'Dep_hour', 'F8': 'Destination', 'F12': 'Arrival_hour', 'F5': 'Dep_minute', 'F10': 'Duration_mins'} | {'F9': 'F11', 'F12': 'F4', 'F10': 'F2', 'F2': 'F1', 'F6': 'F9', 'F1': 'F7', 'F7': 'F6', 'F3': 'F3', 'F11': 'F8', 'F5': 'F12', 'F4': 'F5', 'F8': 'F10'} | {'C1': 'C2', 'C2': 'C1', 'C3': 'C3'} | Low | {'C2': 'Low', 'C1': 'Moderate', 'C3': 'High'} |
SVC | C1 | Australian Credit Approval | Judging by the prediction probabilities, the most probable or likely class assigned by the classifier is C1, with the associated confidence level of 90.97%. The features with the most influence on the prediction above include F9, F13, and F14, while the least important features are F10, F3, and F6. Beside some of the f... | [
"0.43",
"-0.14",
"0.09",
"-0.07",
"0.07",
"-0.06",
"-0.04",
"0.04",
"-0.03",
"0.02",
"0.02",
"-0.01",
"0.01",
"0.00"
] | [
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive"
] | 216 | 127 | {'C2': '9.03%', 'C1': '90.97%'} | [
"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... | [
"F9",
"F13",
"F14",
"F5",
"F8",
"F2",
"F4",
"F1",
"F11",
"F7",
"F12",
"F10",
"F3",
"F6"
] | {'F9': 'A8', 'F13': 'A9', 'F14': 'A12', 'F5': 'A10', 'F8': 'A4', 'F2': 'A14', 'F4': 'A11', 'F1': 'A13', 'F11': 'A1', 'F7': 'A6', 'F12': 'A3', 'F10': 'A5', 'F3': 'A2', 'F6': 'A7'} | {'F8': 'F9', 'F9': 'F13', 'F12': 'F14', 'F10': 'F5', 'F4': 'F8', 'F14': 'F2', 'F11': 'F4', 'F13': 'F1', 'F1': 'F11', 'F6': 'F7', 'F3': 'F12', 'F5': 'F10', 'F2': 'F3', 'F7': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | Class 2 | {'C2': 'Class 1', 'C1': 'Class 2'} |
KNeighborsClassifier | C1 | Credit Risk Classification | According to the model employed, the label for the case is more likely to be C1. This assessment decision is mainly based on the inpacts of features such as F2, F7, F4, F3, and F8. Among these top features, F2, F7, and F4 have positive contributions to the prediction above, while F8 and F3 are identified as negative fe... | [
"0.09",
"0.03",
"0.02",
"-0.02",
"-0.02",
"-0.02",
"-0.01",
"0.01",
"-0.01",
"-0.01",
"0.00"
] | [
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive"
] | 115 | 291 | {'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 (F2, F7, F4 and F8) on the prediction made for this test case.",
"Compare the direction of impact of the features: F3, F5 and F6.",
"Describe the degree of ... | [
"F2",
"F7",
"F4",
"F8",
"F3",
"F5",
"F6",
"F1",
"F9",
"F10",
"F11"
] | {'F2': 'fea_4', 'F7': 'fea_8', 'F4': 'fea_2', 'F8': 'fea_9', 'F3': 'fea_6', 'F5': 'fea_10', 'F6': 'fea_1', 'F1': 'fea_7', 'F9': 'fea_11', 'F10': 'fea_3', 'F11': 'fea_5'} | {'F4': 'F2', 'F8': 'F7', 'F2': 'F4', 'F9': 'F8', 'F6': 'F3', 'F10': 'F5', 'F1': 'F6', 'F7': 'F1', 'F11': 'F9', 'F3': 'F10', 'F5': 'F11'} | {'C1': 'C1', 'C2': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
BernoulliNB | C2 | Personal Loan Modelling | From the prediction likelihood of each class label, the most probable label for the given case based on the values of its features is C2. The likelihood of C1 is negligible, hence we can conclude that the classifier is very confident that C2 is the correct label. Analysing the attributions of the input features showed... | [
"0.34",
"-0.04",
"0.04",
"0.02",
"-0.02",
"0.01",
"0.01",
"-0.00",
"-0.00"
] | [
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 245 | 151 | {'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",
"F3",
"F8",
"F1",
"F5",
"F2",
"F7",
"F6",
"F9"
] | {'F4': 'CD Account', 'F3': 'Income', 'F8': 'CCAvg', 'F1': 'Securities Account', 'F5': 'Education', 'F2': 'Family', 'F7': 'Mortgage', 'F6': 'Age', 'F9': 'Extra_service'} | {'F8': 'F4', 'F2': 'F3', 'F4': 'F8', 'F7': 'F1', 'F5': 'F5', 'F3': 'F2', 'F6': 'F7', 'F1': 'F6', 'F9': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | Reject | {'C2': 'Reject', 'C1': 'Accept'} |
LogisticRegression | C2 | Bike Sharing Demand | The correct label for the given data instance, according to the machine learning algorithm, is C2 and this is mainly because the probability that C1 is the right label is only about 3.08%. From the analysis, the ranking of the input features based on their respective degree of influence is F3, F11, F12, F5, F2, F10, F... | [
"0.48",
"0.36",
"0.20",
"0.14",
"0.09",
"0.08",
"0.07",
"0.06",
"-0.05",
"-0.04",
"-0.02",
"0.02"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive"
] | 225 | 133 | {'C1': '3.08%', 'C2': '96.92%'} | [
"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... | [
"F3",
"F11",
"F12",
"F5",
"F2",
"F10",
"F8",
"F9",
"F6",
"F7",
"F4",
"F1"
] | {'F3': 'Functioning Day', 'F11': 'Rainfall(mm)', 'F12': 'Snowfall (cm)', 'F5': 'Solar Radiation (MJ\\/m2)', 'F2': 'Temperature', 'F10': 'Holiday', 'F8': 'Humidity(%)', 'F9': 'Seasons', 'F6': 'Hour', 'F7': 'Visibility (10m)', 'F4': 'Dew point temperature', 'F1': 'Wind speed (m\\/s)'} | {'F12': 'F3', 'F8': 'F11', 'F9': 'F12', 'F7': 'F5', 'F2': 'F2', 'F11': 'F10', 'F3': 'F8', 'F10': 'F9', 'F1': 'F6', 'F5': 'F7', 'F6': 'F4', 'F4': 'F1'} | {'C1': 'C1', 'C2': 'C2'} | More than 500 | {'C1': 'Less than 500', 'C2': 'More than 500'} |
MLPClassifier | C2 | Hotel Satisfaction | Based on the values of the input variables, the prediction model labels the case given as C2 with very high certainty. Specifically, there is only about a 5.59% possibility that C1 is the correct label according to the model. The most influential factors leading to the above prediction decision are the values of F10, F... | [
"0.27",
"-0.22",
"-0.14",
"-0.07",
"0.06",
"0.05",
"-0.05",
"0.03",
"0.02",
"-0.02",
"-0.01",
"-0.01",
"-0.00",
"-0.00",
"0.00"
] | [
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"positive"
] | 294 | 457 | {'C2': '94.41%', 'C1': '5.59%'} | [
"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... | [
"F10",
"F2",
"F14",
"F15",
"F8",
"F11",
"F12",
"F4",
"F5",
"F1",
"F13",
"F7",
"F9",
"F3",
"F6"
] | {'F10': 'Hotel wifi service', 'F2': 'Type of Travel', 'F14': 'Other service', 'F15': 'Stay comfort', 'F8': 'Type Of Booking', 'F11': 'Ease of Online booking', 'F12': 'Checkin\\/Checkout service', 'F4': 'Age', 'F5': 'Cleanliness', 'F1': 'Food and drink', 'F13': 'Hotel location', 'F7': 'Departure\\/Arrival convenience',... | {'F6': 'F10', 'F3': 'F2', 'F14': 'F14', 'F11': 'F15', 'F4': 'F8', 'F8': 'F11', 'F13': 'F12', 'F5': 'F4', 'F15': 'F5', 'F10': 'F1', 'F9': 'F13', 'F7': 'F7', 'F1': 'F9', 'F2': 'F3', 'F12': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | dissatisfied | {'C2': 'dissatisfied', 'C1': 'satisfied'} |
LogisticRegression | C1 | Airline Passenger Satisfaction | The data under consideration is labelled as C1 since it is the most probable label, with a prediction likelihood equal to 99.97% therefore classifier employed here is very confident that C2 is not the right label. The top features with the greatest influence on the classifier in terms of the above classification are F1... | [
"0.53",
"0.38",
"0.32",
"-0.11",
"0.09",
"0.08",
"0.08",
"-0.07",
"0.06",
"-0.06",
"-0.06",
"0.05",
"0.05",
"-0.04",
"0.04",
"0.04",
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"-0.01",
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] | [
"positive",
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"positive",
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"positive",
"positive",
"positive",
"negative",
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"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negligible",
"negligible"
] | 292 | 183 | {'C2': '0.03%', 'C1': '99.97%'} | [
"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",
"F13",
"F19",
"F20",
"F10",
"F14",
"F3",
"F15",
"F17",
"F11",
"F9",
"F18",
"F22",
"F16",
"F5",
"F4",
"F7",
"F21",
"F8",
"F6",
"F1",
"F12"
] | {'F2': 'Inflight wifi service', 'F13': 'Type of Travel', 'F19': 'Customer Type', 'F20': 'Online boarding', 'F10': 'Inflight service', 'F14': 'Baggage handling', 'F3': 'On-board service', 'F15': 'Departure\\/Arrival time convenient', 'F17': 'Seat comfort', 'F11': 'Inflight entertainment', 'F9': 'Gate location', 'F18': '... | {'F7': 'F2', 'F4': 'F13', 'F2': 'F19', 'F12': 'F20', 'F19': 'F10', 'F17': 'F14', 'F15': 'F3', 'F8': 'F15', 'F13': 'F17', 'F14': 'F11', 'F10': 'F9', 'F20': 'F18', 'F9': 'F22', 'F5': 'F16', 'F16': 'F5', 'F3': 'F4', 'F21': 'F7', 'F22': 'F21', 'F1': 'F8', 'F18': 'F6', 'F11': 'F1', 'F6': 'F12'} | {'C1': 'C2', 'C2': 'C1'} | satisfied | {'C2': 'neutral or dissatisfied', 'C1': 'satisfied'} |
BernoulliNB | C1 | Used Cars Price-Range Prediction | C1 was the predicted category for the given case and the classifier is shown to be very certain about the above prediction verdict, given that the probability of C1 being the label is about 99.72%. The following five features all contributed positively towards the prediction of the C1 class with increasing levels of im... | [
"0.43",
"0.30",
"0.27",
"0.17",
"0.17",
"0.11",
"-0.10",
"0.09",
"-0.04",
"-0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative"
] | 90 | 38 | {'C1': '99.72%', 'C2': '0.28%'} | [
"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 (F7, F2 (value equal to V0) and F8) on the model’s prediction of C1.",
"Su... | [
"F9",
"F6",
"F10",
"F5",
"F4",
"F7",
"F2",
"F8",
"F1",
"F3"
] | {'F9': 'Transmission', 'F6': 'Fuel_Type', 'F10': 'Seats', 'F5': 'Name', 'F4': 'Engine', 'F7': 'car_age', 'F2': 'Owner_Type', 'F8': 'Power', 'F1': 'Mileage', 'F3': 'Kilometers_Driven'} | {'F8': 'F9', 'F7': 'F6', 'F10': 'F10', 'F6': 'F5', 'F3': 'F4', 'F5': 'F7', 'F9': 'F2', 'F4': 'F8', 'F2': 'F1', 'F1': 'F3'} | {'C1': 'C1', 'C2': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
KNeighborsClassifier | C2 | Advertisement Prediction | The ML model or algorithm employed here predicted the class C2 with 100.0% confidence level, clearly implying that the case belongs under the class C2 and not C1 since its associated likelihood is 0.0%. Analysis of the contributions of the features indicated that only features F4 and F7 have negative influence, shiftin... | [
"0.42",
"0.27",
"0.16",
"0.06",
"0.05",
"-0.03",
"-0.03"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 49 | 17 | {'C2': '100.00%', 'C1': '0.00%'} | [
"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: F3 and F2.",
"Compare and contrast the impact of the following features (F6, F1, F5 (with a value equal to V6) and F7 (with a value equal to ... | [
"F3",
"F2",
"F6",
"F1",
"F5",
"F7",
"F4"
] | {'F3': 'Daily Internet Usage', 'F2': 'Daily Time Spent on Site', 'F6': 'Age', 'F1': 'Area Income', 'F5': 'ad_day', 'F7': 'ad_month', 'F4': 'Gender'} | {'F4': 'F3', 'F1': 'F2', 'F2': 'F6', 'F3': 'F1', 'F7': 'F5', 'F6': 'F7', 'F5': 'F4'} | {'C1': 'C2', 'C2': 'C1'} | Skip | {'C2': 'Skip', 'C1': 'Watch'} |
BernoulliNB | C2 | Hotel Satisfaction | Judging from the values of the input variables, the label predicted for the case under consideration is C2 with a high confidence level of 98.89%, implying that the probability of C1 being the actual label is just 1.11%. The attribution analysis suggests that F11, F10, and F3 are the most impactful features controlling... | [
"-0.47",
"0.45",
"0.15",
"0.11",
"0.09",
"0.07",
"-0.06",
"0.05",
"0.04",
"-0.04",
"0.04",
"0.03",
"0.03",
"-0.02",
"0.01"
] | [
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive"
] | 16 | 373 | {'C2': '98.89%', 'C1': '1.11%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F15, F5 and F4) on the model’s prediction of C2.",
"Summarize the set of f... | [
"F11",
"F10",
"F3",
"F15",
"F5",
"F4",
"F9",
"F2",
"F7",
"F6",
"F14",
"F12",
"F8",
"F13",
"F1"
] | {'F11': 'Type of Travel', 'F10': 'Type Of Booking', 'F3': 'Common Room entertainment', 'F15': 'Stay comfort', 'F5': 'Cleanliness', 'F4': 'Hotel wifi service', 'F9': 'Other service', 'F2': 'Ease of Online booking', 'F7': 'Age', 'F6': 'Checkin\\/Checkout service', 'F14': 'Food and drink', 'F12': 'Departure\\/Arrival con... | {'F3': 'F11', 'F4': 'F10', 'F12': 'F3', 'F11': 'F15', 'F15': 'F5', 'F6': 'F4', 'F14': 'F9', 'F8': 'F2', 'F5': 'F7', 'F13': 'F6', 'F10': 'F14', 'F7': 'F12', 'F2': 'F8', 'F9': 'F13', 'F1': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | dissatisfied | {'C2': 'dissatisfied', 'C1': 'satisfied'} |
RandomForestClassifier | C3 | Flight Price-Range Classification | There is little to no doubt that C3, among the three classes, is the proper label for this example since its associated predicted probability is 100.0%. F5, F12, and F4 are the variables with the most influence on the labelling output produced here. Furthermore, these variables have a stronger positive influence on the... | [
"0.23",
"0.19",
"0.17",
"0.06",
"-0.06",
"0.05",
"0.04",
"0.01",
"0.01",
"-0.01",
"-0.01",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive"
] | 114 | 238 | {'C3': '100.00%', 'C2': '0.00%', '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... | [
"F12",
"F4",
"F5",
"F8",
"F2",
"F9",
"F6",
"F7",
"F10",
"F3",
"F1",
"F11"
] | {'F12': 'Duration_hours', 'F4': 'Airline', 'F5': 'Total_Stops', 'F8': 'Journey_day', 'F2': 'Source', 'F9': 'Destination', 'F6': 'Journey_month', 'F7': 'Dep_minute', 'F10': 'Arrival_minute', 'F3': 'Arrival_hour', 'F1': 'Duration_mins', 'F11': 'Dep_hour'} | {'F7': 'F12', 'F9': 'F4', 'F12': 'F5', 'F1': 'F8', 'F10': 'F2', 'F11': 'F9', 'F2': 'F6', 'F4': 'F7', 'F6': 'F10', 'F5': 'F3', 'F8': 'F1', 'F3': 'F11'} | {'C1': 'C3', 'C2': 'C2', 'C3': 'C1'} | Low | {'C3': 'Low', 'C2': 'Moderate', 'C1': 'High'} |
BernoulliNB | C1 | Student Job Placement | Here, the model assigned C1 the highest probability, equal to 99.48%, implying that the predictability of C2 is only 0.52%. Per the attribution analysis, only F1 and F9 have negative contributions that decrease the likelihood of the C1 label in favour of the C2 label. F12, F10, F8, and F2 have the highest positive cont... | [
"0.33",
"0.31",
"0.21",
"0.15",
"-0.13",
"0.08",
"0.06",
"0.04",
"0.03",
"-0.01",
"0.01",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive"
] | 21 | 311 | {'C2': '0.52%', 'C1': '99.48%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F2, F10, F1 and F4 (equal to V1)) on the model’s prediction of C1.",
"Sum... | [
"F12",
"F8",
"F2",
"F10",
"F1",
"F4",
"F6",
"F7",
"F3",
"F9",
"F11",
"F5"
] | {'F12': 'workex', 'F8': 'specialisation', 'F2': 'ssc_p', 'F10': 'hsc_p', 'F1': 'degree_p', 'F4': 'gender', 'F6': 'degree_t', 'F7': 'etest_p', 'F3': 'hsc_b', 'F9': 'hsc_s', 'F11': 'ssc_b', 'F5': 'mba_p'} | {'F11': 'F12', 'F12': 'F8', 'F1': 'F2', 'F2': 'F10', 'F3': 'F1', 'F6': 'F4', 'F10': 'F6', 'F4': 'F7', 'F8': 'F3', 'F9': 'F9', 'F7': 'F11', 'F5': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Placed | {'C2': 'Not Placed', 'C1': 'Placed'} |
RandomForestClassifier | C2 | Employee Attrition | The assigned label or class by the prediction algorithm is C2, which happens to be the most probable class predicted with a probability of around 56.0%, consequently, there is a 44.0% chance that perhaps C1 could be the true label instead. The classification assertion above is attributed to the contributions of mainly ... | [
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"neg... | 27 | 380 | {'C1': '44.00%', 'C2': '56.00%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F18 (value equal to V2), F12 (value equal to V1), F30 (with a value equal ... | [
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] | {'F23': 'OverTime', 'F1': 'BusinessTravel', 'F18': 'MaritalStatus', 'F12': 'JobInvolvement', 'F30': 'WorkLifeBalance', 'F10': 'Education', 'F11': 'EnvironmentSatisfaction', 'F16': 'Gender', 'F29': 'JobRole', 'F24': 'NumCompaniesWorked', 'F8': 'YearsInCurrentRole', 'F15': 'HourlyRate', 'F7': 'Department', 'F28': 'Relati... | {'F26': 'F23', 'F17': 'F1', 'F25': 'F18', 'F29': 'F12', 'F20': 'F30', 'F27': 'F10', 'F28': 'F11', 'F23': 'F16', 'F24': 'F29', 'F8': 'F24', 'F14': 'F8', 'F4': 'F15', 'F21': 'F7', 'F18': 'F28', 'F19': 'F9', 'F16': 'F17', 'F1': 'F14', 'F7': 'F6', 'F10': 'F25', 'F30': 'F26', 'F2': 'F5', 'F15': 'F2', 'F13': 'F4', 'F12': 'F2... | {'C1': 'C1', 'C2': 'C2'} | Leave | {'C1': 'Leave', 'C2': 'Leave'} |
GradientBoostingClassifier | C1 | Broadband Sevice Signup | For the given case, the model predicts C1 as the label. The probability that the label could be the alternative class, C2, is only about 1.94% which implies that the model is very confident in this classification decision or output. F5 and F1 are the top features pushing for the C1 prediction for this case. Other featu... | [
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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: F9 and F1.",
"Compare and contrast the impact of the following features (F22, F6, F12 (with a value equal to V1) and F11) on the model’s pred... | [
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RandomForestClassifier | C1 | E-Commerce Shipping | The predicted likelihood of C1 based on the information supplied to the model is 51.62%, whereas there is a 48.38% likelihood that C2 is the correct label. The uncertainty of the model in terms of this case or instance can be attributed mainly to the direction of influence of the variables F5, F2, and F8. Decreasing th... | [
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"negative",
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] | 163 | 89 | {'C1': '51.62%', 'C2': '48.38%'} | [
"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, F7, F9 and F1) on the model’s prediction of C1.",
"Summarize the set o... | [
"F5",
"F2",
"F8",
"F7",
"F9",
"F1",
"F10",
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] | {'F5': 'Discount_offered', 'F2': 'Weight_in_gms', 'F8': 'Customer_care_calls', 'F7': 'Product_importance', 'F9': 'Mode_of_Shipment', 'F1': 'Warehouse_block', 'F10': 'Cost_of_the_Product', 'F6': 'Gender', 'F3': 'Customer_rating', 'F4': 'Prior_purchases'} | {'F2': 'F5', 'F3': 'F2', 'F6': 'F8', 'F9': 'F7', 'F5': 'F9', 'F4': 'F1', 'F1': 'F10', 'F10': 'F6', 'F7': 'F3', 'F8': 'F4'} | {'C1': 'C1', 'C2': 'C2'} | On-time | {'C1': 'On-time', 'C2': 'Late'} |
SVC | C1 | Tic-Tac-Toe Strategy | In this case, the classifier indicates that there is a 99.50% chance that the C1 class is the true label, so it is correct to conclude that the classifier is not sure that C2 is the correct label for the case here. According to the study, five input variables contradict the label choice, while four variables support th... | [
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"negative",
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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... | [
"F1",
"F3",
"F6",
"F9",
"F2",
"F7",
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] | {'F1': 'middle-middle-square', 'F3': 'top-left-square', 'F6': 'bottom-left-square', 'F9': 'bottom-right-square', 'F2': ' top-right-square', 'F7': 'middle-right-square', 'F8': 'top-middle-square', 'F4': 'middle-left-square', 'F5': 'bottom-middle-square'} | {'F5': 'F1', 'F1': 'F3', 'F7': 'F6', 'F9': 'F9', 'F3': 'F2', 'F6': 'F7', 'F2': 'F8', 'F4': 'F4', 'F8': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | player B win | {'C2': 'player B lose', 'C1': 'player B win'} |
LogisticRegression | C1 | Employee Promotion Prediction | Considering the values of features such as F9, F8, and F11, the model is very certain (about 99.65% certain) that C1 is the right label for the given case. While F9, F8, and F11 are the most important features, the model paid little attention to F1, F5, and F7 when deciding on the appropriate label here.Overall, drivin... | [
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"negative",
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"positive",
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] | 270 | 455 | {'C1': '99.65%', 'C2': '0.35%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F8",
"F9",
"F11",
"F10",
"F4",
"F2",
"F6",
"F3",
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] | {'F8': 'avg_training_score', 'F9': 'KPIs_met >80%', 'F11': 'department', 'F10': 'age', 'F4': 'gender', 'F2': 'region', 'F6': 'length_of_service', 'F3': 'recruitment_channel', 'F1': 'previous_year_rating', 'F5': 'no_of_trainings', 'F7': 'education'} | {'F11': 'F8', 'F10': 'F9', 'F1': 'F11', 'F7': 'F10', 'F4': 'F4', 'F2': 'F2', 'F9': 'F6', 'F5': 'F3', 'F8': 'F1', 'F6': 'F5', 'F3': 'F7'} | {'C1': 'C1', 'C2': 'C2'} | Ignore | {'C1': 'Ignore', 'C2': 'Promote'} |
KNeighborsClassifier | C2 | Suspicious Bidding Identification | With a certainty of 100.0%, the model labels this case as C2 and from the predicted likelihoods across the classes, it can be inferred that the model verdict is that there is a zero chance that the case is under C1. The most significant feature is F1, while the least important attributes are F8, F7, and F3. The moderat... | [
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"positive",
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] | 139 | 69 | {'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 (F1 and F4) on the prediction made for this test case.",
"Compare the direction of impact of the features: F9, F6, F5 and F2.",
"Describe the degree of impa... | [
"F1",
"F4",
"F9",
"F6",
"F5",
"F2",
"F8",
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] | {'F1': 'Z3', 'F4': 'Z9', 'F9': 'Z4', 'F6': 'Z8', 'F5': 'Z1', 'F2': 'Z5', 'F8': 'Z2', 'F7': 'Z6', 'F3': 'Z7'} | {'F3': 'F1', 'F9': 'F4', 'F4': 'F9', 'F8': 'F6', 'F1': 'F5', 'F5': 'F2', 'F2': 'F8', 'F6': 'F7', 'F7': 'F3'} | {'C1': 'C2', 'C2': 'C1'} | Normal | {'C2': 'Normal', 'C1': 'Suspicious'} |
BernoulliNB | C2 | Cab Surge Pricing System | The case under consideration can be labelled as either C2 or C1 or C3, and based on values for features such as F1, F6, F12, F4, and F5, the model labelled this test case as C2 with a confidence level equal to 62.29%. However, there is a 28.41% chance that the label could be C1 and a 9.3% chance that it could be C3. Al... | [
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"positive",
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"positive",
"negative",
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"negative",
"negative",
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] | 92 | 427 | {'C2': '62.29%', 'C1': '28.41%', 'C3': '9.30%'} | [
"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 (when it is equal to V0) and F6 (value equal to V2).",
"Compare and contrast the impact of the following features (F12, F4 (equal to V5)... | [
"F1",
"F6",
"F12",
"F4",
"F5",
"F11",
"F10",
"F9",
"F3",
"F8",
"F2",
"F7"
] | {'F1': 'Confidence_Life_Style_Index', 'F6': 'Destination_Type', 'F12': 'Customer_Rating', 'F4': 'Type_of_Cab', 'F5': 'Cancellation_Last_1Month', 'F11': 'Trip_Distance', 'F10': 'Var1', 'F9': 'Customer_Since_Months', 'F3': 'Gender', 'F8': 'Var3', 'F2': 'Life_Style_Index', 'F7': 'Var2'} | {'F5': 'F1', 'F6': 'F6', 'F7': 'F12', 'F2': 'F4', 'F8': 'F5', 'F1': 'F11', 'F9': 'F10', 'F3': 'F9', 'F12': 'F3', 'F11': 'F8', 'F4': 'F2', 'F10': 'F7'} | {'C1': 'C2', 'C2': 'C1', 'C3': 'C3'} | C1 | {'C2': 'Low', 'C1': 'Medium', 'C3': 'High'} |
SVC | C1 | Advertisement Prediction | Tasked with labelling a given case as either class C1 or class C2 , the model assigns C1 as the most probable true label, with a confidence level of approximately 99.90%. This confidence level suggests that the probability of C2 being the correct label is only 0.10%. Attribution analysis conducted indicates that all t... | [
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] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 42 | 398 | {'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... | [
"F2",
"F6",
"F3",
"F1",
"F5",
"F4",
"F7"
] | {'F2': 'Daily Internet Usage', 'F6': 'Daily Time Spent on Site', 'F3': 'Age', 'F1': 'ad_day', 'F5': 'Area Income', 'F4': 'Gender', 'F7': 'ad_month'} | {'F4': 'F2', 'F1': 'F6', 'F2': 'F3', 'F7': 'F1', 'F3': 'F5', 'F5': 'F4', 'F6': 'F7'} | {'C1': 'C1', 'C2': 'C2'} | Skip | {'C1': 'Skip', 'C2': 'Watch'} |
SVC | C2 | Water Quality Classification | Even though there is moderately high confidence in the assigned label, the prediction probabilities across the two classes indicate that C1 could be the correct label for this data instance. The variables with primary contributions resulting in the labelling decision above are F3, F2, F5, and F4. As per the attributio... | [
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"negative",
"negative",
"positive",
"positive",
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] | 237 | 143 | {'C1': '38.68%', 'C2': '61.32%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F3",
"F2",
"F5",
"F4",
"F6",
"F8",
"F1",
"F7",
"F9"
] | {'F3': 'Sulfate', 'F2': 'Hardness', 'F5': 'ph', 'F4': 'Conductivity', 'F6': 'Turbidity', 'F8': 'Chloramines', 'F1': 'Solids', 'F7': 'Trihalomethanes', 'F9': 'Organic_carbon'} | {'F5': 'F3', 'F2': 'F2', 'F1': 'F5', 'F6': 'F4', 'F9': 'F6', 'F4': 'F8', 'F3': 'F1', 'F8': 'F7', 'F7': 'F9'} | {'C1': 'C1', 'C2': 'C2'} | Portable | {'C1': 'Not Portable', 'C2': 'Portable'} |
RandomForestClassifier | C2 | Student Job Placement | According to the classification model employed here, there is a marginal chance that the true label for this test example is C1. Undoubtedly, the model estimated that the likelihood of the true label being equal to C2 is 99.92%. The above prediction decision is based on the influence of features such as F3, F1, F2, F5,... | [
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] | 96 | 42 | {'C1': '0.08%', 'C2': '99.92%'} | [
"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, F2 (equal to V1), F3, F5 (when it is equal to V0) and F7 (when it is equal to V1)) on the prediction made for this test case.",
"Compare the direction... | [
"F1",
"F2",
"F3",
"F5",
"F7",
"F6",
"F8",
"F4",
"F9",
"F10",
"F11",
"F12"
] | {'F1': 'ssc_p', 'F2': 'workex', 'F3': 'hsc_p', 'F5': 'specialisation', 'F7': 'gender', 'F6': 'mba_p', 'F8': 'hsc_s', 'F4': 'ssc_b', 'F9': 'degree_t', 'F10': 'hsc_b', 'F11': 'degree_p', 'F12': 'etest_p'} | {'F1': 'F1', 'F11': 'F2', 'F2': 'F3', 'F12': 'F5', 'F6': 'F7', 'F5': 'F6', 'F9': 'F8', 'F7': 'F4', 'F10': 'F9', 'F8': 'F10', 'F3': 'F11', 'F4': 'F12'} | {'C1': 'C1', 'C2': 'C2'} | Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
SVM | C2 | Customer Churn Modelling | Considering the values of the features, the prediction from the model for the case under consideration is C2 and this labelling decision is not 100% certain given that there is a 27.27% probability that it could be C1. For the case under consideration, the assigned label is mainly due to the values of the features F3, ... | [
"0.35",
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"0.10",
"0.07",
"0.05",
"-0.03",
"-0.02",
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] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive"
] | 145 | 74 | {'C1': '27.27%', 'C2': '72.73%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the predic... | [
"F3",
"F9",
"F7",
"F2",
"F4",
"F6",
"F10",
"F8",
"F5",
"F1"
] | {'F3': 'Age', 'F9': 'IsActiveMember', 'F7': 'Geography', 'F2': 'NumOfProducts', 'F4': 'Gender', 'F6': 'Tenure', 'F10': 'CreditScore', 'F8': 'EstimatedSalary', 'F5': 'Balance', 'F1': 'HasCrCard'} | {'F4': 'F3', 'F9': 'F9', 'F2': 'F7', 'F7': 'F2', 'F3': 'F4', 'F5': 'F6', 'F1': 'F10', 'F10': 'F8', 'F6': 'F5', 'F8': 'F1'} | {'C1': 'C1', 'C2': 'C2'} | Leave | {'C1': 'Stay', 'C2': 'Leave'} |
DNN | C1 | Concrete Strength Classification | For this case, the classification model's confidence is only about 69.40%, implying that the likelihood of label C2 is about 30.60%. According to the classification attribution analysis, F1 and F2 are the most relevant features, whereas F4 and F6 are the least influential. When the attributions of the features were car... | [
"0.62",
"0.40",
"-0.21",
"-0.10",
"0.09",
"-0.09",
"0.01",
"0.00"
] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive"
] | 269 | 177 | {'C1': '69.40%', 'C2': '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... | [
"F1",
"F2",
"F8",
"F5",
"F3",
"F7",
"F4",
"F6"
] | {'F1': 'slag', 'F2': 'water', 'F8': 'cement', 'F5': 'fineaggregate', 'F3': 'flyash', 'F7': 'coarseaggregate', 'F4': 'age_days', 'F6': 'superplasticizer'} | {'F2': 'F1', 'F4': 'F2', 'F1': 'F8', 'F7': 'F5', 'F3': 'F3', 'F6': 'F7', 'F8': 'F4', 'F5': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | Weak | {'C1': 'Weak', 'C2': 'Strong'} |
LogisticRegression | C2 | Hotel Satisfaction | The model prediction for the test case is C2 and the confidence level of this prediction decision is 91.36%, while the predicted probability of C1 is only 8.64%. According to the attribution analysis, we can see that the features F10 and F8 have negative attributions, pushing the prediction decision towards the alterna... | [
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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 (value equal to V0) and F8 (with a value equal to V0).",
"Compare and contrast the impact of the following features (F9, F13, F1 and F2)... | [
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] | {'F10': 'Type of Travel', 'F8': 'Type Of Booking', 'F9': 'Hotel wifi service', 'F13': 'Common Room entertainment', 'F1': 'Stay comfort', 'F2': 'Other service', 'F14': 'Checkin\\/Checkout service', 'F6': 'Hotel location', 'F15': 'Food and drink', 'F12': 'Cleanliness', 'F11': 'Age', 'F7': 'Departure\\/Arrival convenienc... | {'F3': 'F10', 'F4': 'F8', 'F6': 'F9', 'F12': 'F13', 'F11': 'F1', 'F14': 'F2', 'F13': 'F14', 'F9': 'F6', 'F10': 'F15', 'F15': 'F12', 'F5': 'F11', 'F7': 'F7', 'F2': 'F3', 'F8': 'F4', 'F1': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | dissatisfied | {'C2': 'dissatisfied', 'C1': 'satisfied'} |
RandomForestClassifier | C2 | Student Job Placement | In summary, the model predicted an 87.14% likelihood of the class label C2 for the test example under consideration, therefore, there is a chance of about 12.86% that the correct class label could be a different label. The features with the highest impact on the model are F7, F3, F4, and F10, whose values are attributi... | [
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"positive",
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"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
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] | 30 | 10 | {'C2': '87.14%', 'C1': '12.86%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F4, F10 (with a value equal to V1), F5 (value equal to V1) and F9 (when it... | [
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"F4",
"F10",
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"F12",
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] | {'F7': 'ssc_p', 'F3': 'hsc_p', 'F4': 'degree_p', 'F10': 'workex', 'F5': 'specialisation', 'F9': 'gender', 'F12': 'hsc_s', 'F1': 'etest_p', 'F6': 'degree_t', 'F2': 'mba_p', 'F8': 'ssc_b', 'F11': 'hsc_b'} | {'F1': 'F7', 'F2': 'F3', 'F3': 'F4', 'F11': 'F10', 'F12': 'F5', 'F6': 'F9', 'F9': 'F12', 'F4': 'F1', 'F10': 'F6', 'F5': 'F2', 'F7': 'F8', 'F8': 'F11'} | {'C1': 'C2', 'C2': 'C1'} | Not Placed | {'C2': 'Not Placed', 'C1': 'Placed'} |
LogisticRegression | C1 | Food Ordering Customer Churn Prediction | Mainly based on the values of the features F23, F13, F24, and F30, the model classifies the given case as C1 with a prediction confidence level of 90.15%. This means that there is only a 9.85% chance that the correct label could be C2. The features that positively contribute to the prediction include F23, F30, F43, and... | [
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"neg... | 200 | 115 | {'C2': '9.85%', 'C1': '90.15%'} | [
"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... | [
"F23",
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... | {'F23': 'Unaffordable', 'F13': 'Perference(P2)', 'F24': 'Influence of rating', 'F30': 'Good Food quality', 'F43': 'Delay of delivery person picking up food', 'F18': 'Less Delivery time', 'F27': 'Freshness ', 'F5': 'Politeness', 'F21': 'Ease and convenient', 'F31': 'More restaurant choices', 'F6': 'Missing item', 'F39':... | {'F23': 'F23', 'F9': 'F13', 'F38': 'F24', 'F15': 'F30', 'F26': 'F43', 'F39': 'F18', 'F43': 'F27', 'F42': 'F5', 'F10': 'F21', 'F12': 'F31', 'F28': 'F6', 'F31': 'F39', 'F2': 'F38', 'F11': 'F19', 'F22': 'F20', 'F19': 'F8', 'F44': 'F2', 'F40': 'F16', 'F24': 'F9', 'F20': 'F33', 'F36': 'F26', 'F37': 'F4', 'F41': 'F7', 'F34':... | {'C1': 'C2', 'C2': 'C1'} | Go Away | {'C2': 'Return', 'C1': 'Go Away'} |
SVC | C2 | Bike Sharing Demand | 90.58% it the predicted chance that C2 is the correct label for the given case, indicating that the predicted probability of C1 is only 9.42%. Per the feature-attributions, the top-ranked features are F12, F7, and F6, whereas the smallest important or least ranked features are F3, F11, F10, and F9. The influence of int... | [
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"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
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] | 260 | 170 | {'C2': '90.58%', 'C1': '9.42%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F12",
"F6",
"F7",
"F4",
"F5",
"F2",
"F1",
"F8",
"F3",
"F11",
"F10",
"F9"
] | {'F12': 'Functioning Day', 'F6': 'Solar Radiation (MJ\\/m2)', 'F7': 'Rainfall(mm)', 'F4': 'Snowfall (cm)', 'F5': 'Hour', 'F2': 'Temperature', 'F1': 'Holiday', 'F8': 'Humidity(%)', 'F3': 'Visibility (10m)', 'F11': 'Dew point temperature', 'F10': 'Seasons', 'F9': 'Wind speed (m\\/s)'} | {'F12': 'F12', 'F7': 'F6', 'F8': 'F7', 'F9': 'F4', 'F1': 'F5', 'F2': 'F2', 'F11': 'F1', 'F3': 'F8', 'F5': 'F3', 'F6': 'F11', 'F10': 'F10', 'F4': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | Less than 500 | {'C2': 'Less than 500', 'C1': 'More than 500'} |
GradientBoostingClassifier | C1 | Paris House Classification | The most likely label for the given scenario, according to this prediction, is C1, which has a prediction probability of 97.02 percent, whereas C2 has a prediction probability of just 2.98 percent. The impact of F14, F7, and F1 is mostly responsible for the aforementioned classification. F13, F12, and F17 are the follo... | [
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"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"positive"
] | 255 | 338 | {'C2': '2.98%', 'C1': '97.02%'} | [
"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",
"F7",
"F1",
"F13",
"F12",
"F17",
"F11",
"F6",
"F4",
"F5",
"F15",
"F10",
"F3",
"F16",
"F9",
"F2",
"F8"
] | {'F14': 'isNewBuilt', 'F7': 'hasYard', 'F1': 'hasPool', 'F13': 'hasStormProtector', 'F12': 'made', 'F17': 'squareMeters', 'F11': 'floors', 'F6': 'cityCode', 'F4': 'hasGuestRoom', 'F5': 'basement', 'F15': 'numPrevOwners', 'F10': 'price', 'F3': 'numberOfRooms', 'F16': 'garage', 'F9': 'cityPartRange', 'F2': 'hasStorageRoo... | {'F3': 'F14', 'F1': 'F7', 'F2': 'F1', 'F4': 'F13', 'F12': 'F12', 'F6': 'F17', 'F8': 'F11', 'F9': 'F6', 'F16': 'F4', 'F13': 'F5', 'F11': 'F15', 'F17': 'F10', 'F7': 'F3', 'F15': 'F16', 'F10': 'F9', 'F5': 'F2', 'F14': 'F8'} | {'C1': 'C2', 'C2': 'C1'} | Luxury | {'C2': 'Basic', 'C1': 'Luxury'} |
GradientBoostingClassifier | C2 | German Credit Evaluation | According to the prediction algorithm employed here, the most probable label for the given data instance is C2. The confidence level associated with the prediction decision above is 64.62%, meaning there is about a 35.38% likelihood that C1 is the right choice. The input features can be ranked according to their respe... | [
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"negative",
"positive",
"negative",
"negative",
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] | 230 | 137 | {'C2': '64.62%', 'C1': '35.38%'} | [
"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",
"F7",
"F5",
"F9",
"F2",
"F8",
"F1",
"F3",
"F4"
] | {'F6': 'Saving accounts', 'F7': 'Sex', 'F5': 'Duration', 'F9': 'Housing', 'F2': 'Checking account', 'F8': 'Purpose', 'F1': 'Credit amount', 'F3': 'Age', 'F4': 'Job'} | {'F5': 'F6', 'F2': 'F7', 'F8': 'F5', 'F4': 'F9', 'F6': 'F2', 'F9': 'F8', 'F7': 'F1', 'F1': 'F3', 'F3': 'F4'} | {'C1': 'C2', 'C2': 'C1'} | Good Credit | {'C2': 'Good Credit', 'C1': 'Bad Credit'} |
KNeighborsClassifier | C2 | Tic-Tac-Toe Strategy | The true label has a 50.0% chance of being one of the two classes and based on the predicted likelihoods mentioned above, it can be concluded that the model is very unsure about the correctness of the classification. The above prediction decisions are mainly influenced by the features F2, F3, F4, F1, F5, and F7, while ... | [
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] | [
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive"
] | 212 | 252 | {'C2': '50.00%', 'C1': '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... | [
"F2",
"F3",
"F7",
"F4",
"F1",
"F5",
"F8",
"F6",
"F9"
] | {'F2': 'middle-middle-square', 'F3': 'top-left-square', 'F7': 'bottom-left-square', 'F4': 'bottom-right-square', 'F1': 'top-middle-square', 'F5': ' top-right-square', 'F8': 'middle-right-square', 'F6': 'bottom-middle-square', 'F9': 'middle-left-square'} | {'F5': 'F2', 'F1': 'F3', 'F7': 'F7', 'F9': 'F4', 'F2': 'F1', 'F3': 'F5', 'F6': 'F8', 'F8': 'F6', 'F4': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | player B lose | {'C2': 'player B lose', 'C1': 'player B win'} |
DecisionTreeClassifier | C1 | Credit Risk Classification | The model is assigned the label C1 for the given example. F10, F2, and F4 are the most important features that influence the above-mentioned estimate decision, however unlike them, F6, F1, and F11 are less important. The majority of features have values that swing the judgement towards the other label, C2. The only inp... | [
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] | [
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
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] | 131 | 278 | {'C2': '0.00%', 'C1': '100.00%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the predic... | [
"F10",
"F2",
"F4",
"F9",
"F3",
"F8",
"F7",
"F5",
"F6",
"F1",
"F11"
] | {'F10': 'fea_4', 'F2': 'fea_8', 'F4': 'fea_5', 'F9': 'fea_2', 'F3': 'fea_1', 'F8': 'fea_9', 'F7': 'fea_11', 'F5': 'fea_6', 'F6': 'fea_10', 'F1': 'fea_7', 'F11': 'fea_3'} | {'F4': 'F10', 'F8': 'F2', 'F5': 'F4', 'F2': 'F9', 'F1': 'F3', 'F9': 'F8', 'F11': 'F7', 'F6': 'F5', 'F10': 'F6', 'F7': 'F1', 'F3': 'F11'} | {'C1': 'C2', 'C2': 'C1'} | High | {'C2': 'Low', 'C1': 'High'} |
RandomForestClassifier | C1 | Annual Income Earnings | Since the probability that C2 is the correct label is only 2.18%, the classifier assigns the label C1 in this labelling instance. The main factors influencing this classification decision are the values of the variables F4, F11, F9, and F7. From inspecting the direction of influence of the above-mentioned variables, th... | [
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"positive",
"negative",
"positive",
"negative",
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"positive",
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] | 164 | 221 | {'C1': '97.82%', 'C2': '2.18%'} | [
"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 (F7, F6 and F8) on the model’s prediction of C1.",
"Summarize the set of fe... | [
"F4",
"F11",
"F9",
"F7",
"F6",
"F8",
"F5",
"F2",
"F12",
"F10",
"F1",
"F14",
"F3",
"F13"
] | {'F4': 'Capital Gain', 'F11': 'Marital Status', 'F9': 'Relationship', 'F7': 'Age', 'F6': 'Education-Num', 'F8': 'Hours per week', 'F5': 'Occupation', 'F2': 'Capital Loss', 'F12': 'Sex', 'F10': 'Education', 'F1': 'Race', 'F14': 'fnlwgt', 'F3': 'Country', 'F13': 'Workclass'} | {'F11': 'F4', 'F6': 'F11', 'F8': 'F9', 'F1': 'F7', 'F5': 'F6', 'F13': 'F8', 'F7': 'F5', 'F12': 'F2', 'F10': 'F12', 'F4': 'F10', 'F9': 'F1', 'F3': 'F14', 'F14': 'F3', 'F2': 'F13'} | {'C1': 'C1', 'C2': 'C2'} | Under 50K | {'C1': 'Under 50K', 'C2': 'Above 50K'} |
RandomForestClassifier | C2 | Broadband Sevice Signup | The selected case is labelled as C2 with close to an 85.0% confidence level, hinting that there is a smaller chance that it could be C1. The most important variables when determining the label for this case are F15, F13, F42, and F21. The variables with moderate influence include F10, F25, F11, and F27. However, the la... | [
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"neg... | 169 | 95 | {'C1': '15.00%', 'C2': '85.00%'} | [
"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: F15, F13 and F42.",
"Compare and contrast the impact of the following features (F21, F20 and F17) on the model’s prediction of C2.",
"Descri... | [
"F15",
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"... | {'F15': 'X32', 'F13': 'X38', 'F42': 'X35', 'F21': 'X24', 'F20': 'X31', 'F17': 'X19', 'F10': 'X42', 'F25': 'X12', 'F11': 'X21', 'F27': 'X7', 'F31': 'X22', 'F22': 'X27', 'F4': 'X11', 'F9': 'X41', 'F7': 'X1', 'F39': 'X16', 'F30': 'X33', 'F26': 'X6', 'F12': 'X10', 'F16': 'X4', 'F24': 'X5', 'F18': 'X37', 'F29': 'X39', 'F3':... | {'F29': 'F15', 'F35': 'F13', 'F32': 'F42', 'F22': 'F21', 'F28': 'F20', 'F17': 'F17', 'F38': 'F10', 'F10': 'F25', 'F19': 'F11', 'F5': 'F27', 'F20': 'F31', 'F25': 'F22', 'F9': 'F4', 'F39': 'F9', 'F40': 'F7', 'F14': 'F39', 'F30': 'F30', 'F4': 'F26', 'F8': 'F12', 'F3': 'F16', 'F41': 'F24', 'F34': 'F18', 'F36': 'F29', 'F37'... | {'C1': 'C1', 'C2': 'C2'} | Yes | {'C1': 'No', 'C2': 'Yes'} |
LogisticRegression | C1 | Australian Credit Approval | The probable label for the given case is C1 since its associated predicted probability is 91.85% compared to the 8.15% of C2. The input variables mostly responsible for the above prediction verdict are F11, F10, and F6, however, the values of F8, F14, and F3 are deemed less relevant by the model in this case. The attri... | [
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"-0.07",
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"0.02",
"-0.01",
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] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative"
] | 410 | 197 | {'C2': '8.15%', 'C1': '91.85%'} | [
"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",
"F10",
"F6",
"F5",
"F12",
"F4",
"F1",
"F9",
"F7",
"F13",
"F2",
"F8",
"F14",
"F3"
] | {'F11': 'A8', 'F10': 'A14', 'F6': 'A9', 'F5': 'A13', 'F12': 'A5', 'F4': 'A11', 'F1': 'A12', 'F9': 'A7', 'F7': 'A4', 'F13': 'A10', 'F2': 'A6', 'F8': 'A1', 'F14': 'A2', 'F3': 'A3'} | {'F8': 'F11', 'F14': 'F10', 'F9': 'F6', 'F13': 'F5', 'F5': 'F12', 'F11': 'F4', 'F12': 'F1', 'F7': 'F9', 'F4': 'F7', 'F10': 'F13', 'F6': 'F2', 'F1': 'F8', 'F2': 'F14', 'F3': 'F3'} | {'C1': 'C2', 'C2': 'C1'} | Class 2 | {'C2': 'Class 1', 'C1': 'Class 2'} |
KNeighborsClassifier | C2 | Tic-Tac-Toe Strategy | The classification verdict of the model for the case under consideration has a 50.10% chance of being C1. But based on the estimated likelihoods indicated above, it is possible to deduce that the model is extremely doubtful about the classification's validity. The following variables have the most attributions to the a... | [
"0.21",
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] | [
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive"
] | 212 | 253 | {'C1': '50.10%', 'C2': '49.90%'} | [
"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",
"F6",
"F9",
"F2",
"F4",
"F7",
"F3",
"F8",
"F5"
] | {'F1': 'middle-middle-square', 'F6': 'top-left-square', 'F9': 'bottom-left-square', 'F2': 'bottom-right-square', 'F4': 'top-middle-square', 'F7': ' top-right-square', 'F3': 'middle-right-square', 'F8': 'bottom-middle-square', 'F5': 'middle-left-square'} | {'F5': 'F1', 'F1': 'F6', 'F7': 'F9', 'F9': 'F2', 'F2': 'F4', 'F3': 'F7', 'F6': 'F3', 'F8': 'F8', 'F4': 'F5'} | {'C2': 'C1', 'C1': 'C2'} | player B lose | {'C1': 'player B win', 'C2': 'player B lose'} |
LogisticRegression | C2 | Used Cars Price-Range Prediction | The algorithm classified the given data as C2 with close to 99.32% certainty since the prediction likelihood of C1 is only 0.68%. The abovementioned prediction verdict is largely due to the influence of F10, F2, and F1 while the other influential features include F7, F3, and F4. However, F5, F8, F9, and F6 are shown t... | [
"0.43",
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] | [
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 217 | 128 | {'C1': '0.68%', 'C2': '99.32%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F10",
"F2",
"F1",
"F7",
"F3",
"F4",
"F5",
"F8",
"F9",
"F6"
] | {'F10': 'Power', 'F2': 'car_age', 'F1': 'Name', 'F7': 'Fuel_Type', 'F3': 'Seats', 'F4': 'Transmission', 'F5': 'Mileage', 'F8': 'Owner_Type', 'F9': 'Kilometers_Driven', 'F6': 'Engine'} | {'F4': 'F10', 'F5': 'F2', 'F6': 'F1', 'F7': 'F7', 'F10': 'F3', 'F8': 'F4', 'F2': 'F5', 'F9': 'F8', 'F1': 'F9', 'F3': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
LogisticRegression | C1 | Flight Price-Range Classification | The model is quite certain that C1 is the most likely class for the current scenario. C1 has a 90.48% chance of being correct, implying that any of the other labels is highly unlikely. F10 and F4 are the most relevant variables influencing the abovementioned classification decision but all other factors or variables ar... | [
"0.40",
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"0.03",
"0.03",
"-0.02",
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] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 89 | 245 | {'C1': '90.48%', 'C3': '9.51%', 'C2': '0.01%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F4 (equal to V4) and F10 (equ... | [
"F4",
"F10",
"F5",
"F1",
"F8",
"F7",
"F2",
"F11",
"F3",
"F12",
"F9",
"F6"
] | {'F4': 'Total_Stops', 'F10': 'Airline', 'F5': 'Destination', 'F1': 'Arrival_hour', 'F8': 'Source', 'F7': 'Duration_hours', 'F2': 'Dep_hour', 'F11': 'Dep_minute', 'F3': 'Arrival_minute', 'F12': 'Journey_month', 'F9': 'Journey_day', 'F6': 'Duration_mins'} | {'F12': 'F4', 'F9': 'F10', 'F11': 'F5', 'F5': 'F1', 'F10': 'F8', 'F7': 'F7', 'F3': 'F2', 'F4': 'F11', 'F6': 'F3', 'F2': 'F12', 'F1': 'F9', 'F8': 'F6'} | {'C1': 'C1', 'C2': 'C3', 'C3': 'C2'} | Low | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
LogisticRegression | C2 | Suspicious Bidding Identification | The label assigned by the model is C2 with a higher predicted confidence level of 99.99%, meaning the probability of C1 being the correct label is virtually equal to zero. The classification decision above is mainly due to the influence of the features F5, F3, F8, and F4, however, the remaining features have very margi... | [
"0.52",
"0.07",
"0.01",
"0.01",
"0.01",
"0.00",
"-0.00",
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] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative"
] | 199 | 114 | {'C2': '99.99%', 'C1': '0.01%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F4, F9 and F7) on the model’s prediction of C2.",
"Summarize the set of fe... | [
"F5",
"F3",
"F8",
"F4",
"F9",
"F7",
"F1",
"F2",
"F6"
] | {'F5': 'Z3', 'F3': 'Z8', 'F8': 'Z4', 'F4': 'Z2', 'F9': 'Z5', 'F7': 'Z7', 'F1': 'Z1', 'F2': 'Z6', 'F6': 'Z9'} | {'F3': 'F5', 'F8': 'F3', 'F4': 'F8', 'F2': 'F4', 'F5': 'F9', 'F7': 'F7', 'F1': 'F1', 'F6': 'F2', 'F9': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | Normal | {'C2': 'Normal', 'C1': 'Suspicious'} |
SVC | C1 | German Credit Evaluation | With respect to the given case, the classification algorithm employed here generates C1 as the most probable class since the probability of C2 is 41.63% while that of C1 is 58.37%. F1, F7, and F4 are the most influential features resulting in the classification decision mentioned above, whereas the least relevant featu... | [
"-0.11",
"-0.06",
"-0.06",
"0.04",
"0.03",
"0.02",
"-0.01",
"0.00",
"0.00"
] | [
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive"
] | 407 | 461 | {'C1': '58.37%', 'C2': '41.63%'} | [
"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",
"F4",
"F6",
"F3",
"F9",
"F8",
"F2",
"F5"
] | {'F1': 'Checking account', 'F7': 'Duration', 'F4': 'Saving accounts', 'F6': 'Sex', 'F3': 'Purpose', 'F9': 'Age', 'F8': 'Housing', 'F2': 'Job', 'F5': 'Credit amount'} | {'F6': 'F1', 'F8': 'F7', 'F5': 'F4', 'F2': 'F6', 'F9': 'F3', 'F1': 'F9', 'F4': 'F8', 'F3': 'F2', 'F7': 'F5'} | {'C1': 'C1', 'C2': 'C2'} | Good Credit | {'C1': 'Good Credit', 'C2': 'Bad Credit'} |
GradientBoostingClassifier | C1 | German Credit Evaluation | The most likely label for the provided data instance, according to the predictive algorithm used here, is C1. The confidence level associated with the above prediction decision is 64.62 percent, which means C2 has a 35.38 percent chance of being correct. The following input features can be prioritised in decreasing ord... | [
"-0.11",
"0.08",
"-0.08",
"-0.06",
"0.06",
"0.03",
"-0.03",
"0.01",
"0.00"
] | [
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive"
] | 230 | 306 | {'C1': '64.62%', 'C2': '35.38%'} | [
"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",
"F9",
"F8",
"F1",
"F4",
"F6",
"F2",
"F7",
"F3"
] | {'F5': 'Saving accounts', 'F9': 'Sex', 'F8': 'Duration', 'F1': 'Housing', 'F4': 'Checking account', 'F6': 'Purpose', 'F2': 'Credit amount', 'F7': 'Age', 'F3': 'Job'} | {'F5': 'F5', 'F2': 'F9', 'F8': 'F8', 'F4': 'F1', 'F6': 'F4', 'F9': 'F6', 'F7': 'F2', 'F1': 'F7', 'F3': 'F3'} | {'C1': 'C1', 'C2': 'C2'} | Good Credit | {'C1': 'Good Credit', 'C2': 'Bad Credit'} |
SVC | C1 | Real Estate Investment | C1 is the label predicted by the classifier for the case or example under consideration the confidence in the above prediction is about 96.35%. It is important to take into consideration, however, that there is also a very small chance equal to 3.65% that the correct label could be C2. The ranking of the features accor... | [
"0.44",
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"0.08",
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"-0.03",
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"-0.01",
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] | [
"positive",
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"negative",
"negative",
"positive",
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"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 148 | 77 | {'C2': '3.65%', 'C1': '96.35%'} | [
"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: F9, F19 and F1.",
"Compare and contrast the impact of the following features (F8, F15 and F13) on the model’s prediction of C1.",
"Describe ... | [
"F9",
"F19",
"F1",
"F8",
"F15",
"F13",
"F18",
"F4",
"F6",
"F12",
"F20",
"F7",
"F10",
"F5",
"F2",
"F16",
"F14",
"F17",
"F11",
"F3"
] | {'F9': 'Feature7', 'F19': 'Feature4', 'F1': 'Feature14', 'F8': 'Feature2', 'F15': 'Feature8', 'F13': 'Feature1', 'F18': 'Feature13', 'F4': 'Feature6', 'F6': 'Feature10', 'F12': 'Feature15', 'F20': 'Feature18', 'F7': 'Feature9', 'F10': 'Feature12', 'F5': 'Feature16', 'F2': 'Feature19', 'F16': 'Feature5', 'F14': 'Feature... | {'F11': 'F9', 'F9': 'F19', 'F17': 'F1', 'F1': 'F8', 'F3': 'F15', 'F7': 'F13', 'F16': 'F18', 'F10': 'F4', 'F13': 'F6', 'F4': 'F12', 'F19': 'F20', 'F12': 'F7', 'F15': 'F10', 'F18': 'F5', 'F5': 'F2', 'F2': 'F16', 'F14': 'F14', 'F20': 'F17', 'F8': 'F11', 'F6': 'F3'} | {'C1': 'C2', 'C2': 'C1'} | Invest | {'C2': 'Ignore', 'C1': 'Invest'} |
DecisionTreeClassifier | C1 | Insurance Churn | The model predicted the C1 class with very high confidence of 93.27%, hence we can conclude that there is only a 6.73% chance that the true label is C2. Two features have a very strong positive influence on the prediction of the C1 class and they are F7 and F3. The following features have a medium impact and are listed... | [
"0.38",
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"-0.00",
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] | [
"positive",
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"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 83 | 32 | {'C2': '6.73%', 'C1': '93.27%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F7",
"F3",
"F5",
"F12",
"F11",
"F1",
"F4",
"F2",
"F9",
"F13",
"F8",
"F15",
"F14",
"F10",
"F16",
"F6"
] | {'F7': 'feature15', 'F3': 'feature14', 'F5': 'feature10', 'F12': 'feature11', 'F11': 'feature5', 'F1': 'feature13', 'F4': 'feature4', 'F2': 'feature3', 'F9': 'feature12', 'F13': 'feature1', 'F8': 'feature7', 'F15': 'feature2', 'F14': 'feature6', 'F10': 'feature0', 'F16': 'feature9', 'F6': 'feature8'} | {'F9': 'F7', 'F8': 'F3', 'F4': 'F5', 'F5': 'F12', 'F15': 'F11', 'F7': 'F1', 'F14': 'F4', 'F13': 'F2', 'F6': 'F9', 'F11': 'F13', 'F1': 'F8', 'F12': 'F15', 'F16': 'F14', 'F10': 'F10', 'F3': 'F16', 'F2': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | Leave | {'C2': 'Stay', 'C1': 'Leave'} |
BernoulliNB | C1 | Customer Churn Modelling | This case or instance is labelled as C1 with a very high confidence level, however, the classifier estimates that C2 could be the correct label with a prediction likelihood of about 5.75%. The values F9, F3, and F1 played a major role in the aforementioned labelling choice and because F4 and F8 have minimal attribution... | [
"0.22",
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"-0.14",
"-0.14",
"-0.12",
"-0.02",
"0.02",
"0.01",
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] | [
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 172 | 271 | {'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: F9 and F3.",
"Compare and contrast the impact of the following features (F1, F7, F10 and F5) on the model’s prediction of C1.",
"Describe th... | [
"F9",
"F3",
"F1",
"F7",
"F10",
"F5",
"F2",
"F6",
"F4",
"F8"
] | {'F9': 'IsActiveMember', 'F3': 'NumOfProducts', 'F1': 'Gender', 'F7': 'Geography', 'F10': 'Age', 'F5': 'CreditScore', 'F2': 'EstimatedSalary', 'F6': 'Balance', 'F4': 'HasCrCard', 'F8': 'Tenure'} | {'F9': 'F9', 'F7': 'F3', 'F3': 'F1', 'F2': 'F7', 'F4': 'F10', 'F1': 'F5', 'F10': 'F2', 'F6': 'F6', 'F8': 'F4', 'F5': 'F8'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Stay', 'C2': 'Leave'} |
LogisticRegression | C1 | Printer Sales | The prediction likelihood of class C1 is 73.85%, making it the most probable label for the given case. When making the above prediction, the input features are shown to have some degree of influence on the decision made by the classifier. While features such as F16, F25, and F19 have very low contributions to the class... | [
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"positive",
"positive",
"negative",
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"neg... | 33 | 388 | {'C1': '73.85%', 'C2': '26.15%'} | [
"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... | [
"F26",
"F22",
"F11",
"F14",
"F13",
"F10",
"F18",
"F12",
"F15",
"F5",
"F6",
"F4",
"F21",
"F7",
"F3",
"F17",
"F1",
"F23",
"F24",
"F9",
"F16",
"F25",
"F19",
"F2",
"F8",
"F20"
] | {'F26': 'X8', 'F22': 'X1', 'F11': 'X24', 'F14': 'X21', 'F13': 'X4', 'F10': 'X18', 'F18': 'X17', 'F12': 'X25', 'F15': 'X7', 'F5': 'X20', 'F6': 'X23', 'F4': 'X9', 'F21': 'X2', 'F7': 'X22', 'F3': 'X16', 'F17': 'X10', 'F1': 'X15', 'F23': 'X14', 'F24': 'X26', 'F9': 'X19', 'F16': 'X13', 'F25': 'X12', 'F19': 'X11', 'F2': 'X6'... | {'F8': 'F26', 'F1': 'F22', 'F24': 'F11', 'F21': 'F14', 'F4': 'F13', 'F18': 'F10', 'F17': 'F18', 'F25': 'F12', 'F7': 'F15', 'F20': 'F5', 'F23': 'F6', 'F9': 'F4', 'F2': 'F21', 'F22': 'F7', 'F16': 'F3', 'F10': 'F17', 'F15': 'F1', 'F14': 'F23', 'F26': 'F24', 'F19': 'F9', 'F13': 'F16', 'F12': 'F25', 'F11': 'F19', 'F6': 'F2'... | {'C1': 'C1', 'C2': 'C2'} | Less | {'C1': 'Less', 'C2': 'More'} |
LogisticRegression | C1 | Printer Sales | According to the model, the probability of C2 is 12.35% and that of C1 is 87.65% meaning C1 is the most probable label for the given case. The variables with the majority influence on the abovementioned decision are F25, F3, F12, F8, F20, and F21 whereas variables F16, F23, F4, F9, F6, and F26 are shown to have little ... | [
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"positive",
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"neg... | 405 | 460 | {'C2': '12.35%', 'C1': '87.65%'} | [
"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... | [
"F25",
"F3",
"F12",
"F8",
"F20",
"F21",
"F19",
"F1",
"F18",
"F24",
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"F2",
"F13",
"F5",
"F14",
"F16",
"F23",
"F4",
"F9",
"F6",
"F26"
] | {'F25': 'X8', 'F3': 'X21', 'F12': 'X1', 'F8': 'X25', 'F20': 'X2', 'F21': 'X24', 'F19': 'X15', 'F1': 'X4', 'F18': 'X20', 'F24': 'X10', 'F17': 'X5', 'F11': 'X6', 'F15': 'X11', 'F7': 'X12', 'F22': 'X9', 'F10': 'X26', 'F2': 'X23', 'F13': 'X7', 'F5': 'X14', 'F14': 'X17', 'F16': 'X18', 'F23': 'X19', 'F4': 'X16', 'F9': 'X13',... | {'F8': 'F25', 'F21': 'F3', 'F1': 'F12', 'F25': 'F8', 'F2': 'F20', 'F24': 'F21', 'F15': 'F19', 'F4': 'F1', 'F20': 'F18', 'F10': 'F24', 'F5': 'F17', 'F6': 'F11', 'F11': 'F15', 'F12': 'F7', 'F9': 'F22', 'F26': 'F10', 'F23': 'F2', 'F7': 'F13', 'F14': 'F5', 'F17': 'F14', 'F18': 'F16', 'F19': 'F23', 'F16': 'F4', 'F13': 'F9',... | {'C1': 'C2', 'C2': 'C1'} | More | {'C2': 'Less', 'C1': 'More'} |
RandomForestClassifier | C1 | Cab Surge Pricing System | With a moderately high level of confidence, C1 is assigned to the given case by the classifier and this is due to the fact that the other classes, C3 and C2, have likelihoods of 3.0% and 14.0%, respectively. Across the input features, only F11, F3, F9, and F12 are shown to contribute negatively, shifting the classifica... | [
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] | 329 | 187 | {'C3': '3.00%', 'C1': '83.00%', 'C2': '14.00%'} | [
"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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] | {'F5': 'Type_of_Cab', 'F11': 'Destination_Type', 'F3': 'Trip_Distance', 'F9': 'Cancellation_Last_1Month', 'F6': 'Confidence_Life_Style_Index', 'F4': 'Var3', 'F12': 'Customer_Since_Months', 'F2': 'Life_Style_Index', 'F1': 'Var2', 'F8': 'Gender', 'F10': 'Var1', 'F7': 'Customer_Rating'} | {'F2': 'F5', 'F6': 'F11', 'F1': 'F3', 'F8': 'F9', 'F5': 'F6', 'F11': 'F4', 'F3': 'F12', 'F4': 'F2', 'F10': 'F1', 'F12': 'F8', 'F9': 'F10', 'F7': 'F7'} | {'C1': 'C3', 'C2': 'C1', 'C3': 'C2'} | C2 | {'C3': 'Low', 'C1': 'Medium', 'C2': 'High'} |
MLPClassifier | C1 | Ethereum Fraud Detection | Considering the values of the input variables, the classification model is very confident that the most probable label is not C2 but C1. The top input variables receiving much consideration from the model to arrive at the classification verdict are F38, F28, F31, F15, and F22. Among these most influential variables, F3... | [
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"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the predic... | [
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SVC | C1 | Job Change of Data Scientists | The odds are in favour of label C1 given that the probability of it being the correct label for the case under consideration is 81.32%. However, the likelihood of label C2 is 18.68%. The classification decision above is mainly due to the values of F8, F12, F9, and F5. The feature with the least attribution to the model... | [
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"positive",
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"negative",
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] | 170 | 437 | {'C1': '81.32%', 'C2': '18.68%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
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] | {'F8': 'city', 'F12': 'company_type', 'F9': 'city_development_index', 'F5': 'education_level', 'F10': 'enrolled_university', 'F2': 'gender', 'F7': 'relevent_experience', 'F1': 'training_hours', 'F3': 'major_discipline', 'F4': 'company_size', 'F11': 'experience', 'F6': 'last_new_job'} | {'F3': 'F8', 'F11': 'F12', 'F1': 'F9', 'F7': 'F5', 'F6': 'F10', 'F4': 'F2', 'F5': 'F7', 'F2': 'F1', 'F8': 'F3', 'F10': 'F4', 'F9': 'F11', 'F12': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Stay', 'C2': 'Leave'} |
DNN | C2 | Concrete Strength Classification | The model predicted C2 with a high probability equal to 88.70%, whereas C1 has only a 11.30% likelihood of being the true label. Considering the predicted likelihood of C1, there is only little confidence in its correctness as the true label for the case here. The value of F4 has a large negative influence on the C2 cl... | [
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"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 17 | 4 | {'C2': '88.70%', 'C1': '11.30%'} | [
"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 and F8.",
"Compare and contrast the impact of the following features (F1, F5, F3 and F2) on the model’s prediction of C2.",
"Describe the... | [
"F4",
"F8",
"F1",
"F5",
"F3",
"F2",
"F7",
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] | {'F4': 'coarseaggregate', 'F8': 'age_days', 'F1': 'superplasticizer', 'F5': 'cement', 'F3': 'water', 'F2': 'fineaggregate', 'F7': 'slag', 'F6': 'flyash'} | {'F6': 'F4', 'F8': 'F8', 'F5': 'F1', 'F1': 'F5', 'F4': 'F3', 'F7': 'F2', 'F2': 'F7', 'F3': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | Weak | {'C2': 'Weak', 'C1': 'Strong'} |
BernoulliNB | C2 | Suspicious Bidding Identification | The algorithm's predicted output label for the given case is C2 with a very strong confidence level equal to 100.0%; hence C1 can't be the true label. Among the features, the most relevant ones are F3, F6, and F8 with very significant impact, pushing the prediction decision away from C1 towards C2. The next set of attr... | [
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"positive",
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"positive",
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] | 126 | 59 | {'C2': '100.00%', 'C1': '0.00%'} | [
"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: F3, F6 (value equal to V1), F8 and F9.",
"Compare and contrast the impact of the following features (F7, F2 and F5) on the model’s prediction... | [
"F3",
"F6",
"F8",
"F9",
"F7",
"F2",
"F5",
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] | {'F3': 'Z3', 'F6': 'Z8', 'F8': 'Z2', 'F9': 'Z7', 'F7': 'Z5', 'F2': 'Z4', 'F5': 'Z6', 'F4': 'Z1', 'F1': 'Z9'} | {'F3': 'F3', 'F8': 'F6', 'F2': 'F8', 'F7': 'F9', 'F5': 'F7', 'F4': 'F2', 'F6': 'F5', 'F1': 'F4', 'F9': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Normal | {'C2': 'Normal', 'C1': 'Suspicious'} |
RandomForestClassifier | C2 | Health Care Services Satisfaction Prediction | In this case, the prediction algorithm is not 100.0% certain that the correct label for the given case is C2, since there is a 43.49% chance that the right label could be C1 instead. The algorithm's decision to label the case as C2 mainly stems from the influence of features such as F1, F12, F15, F3, and F14. On the ot... | [
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] | 252 | 162 | {'C1': '43.49%', 'C2': '56.51%'} | [
"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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"F13",
"F8",
"F2",
"F11",
"F16",
"F5",
"F10",
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] | {'F1': 'Quality\\/experience dr.', 'F12': 'Exact diagnosis', 'F15': 'Hygiene and cleaning', 'F14': 'Specialists avaliable', 'F3': 'Modern equipment', 'F4': 'hospital rooms quality', 'F13': 'Admin procedures', 'F8': 'avaliablity of drugs', 'F2': 'parking, playing rooms, caffes', 'F11': 'Time waiting', 'F16': 'friendly h... | {'F6': 'F1', 'F9': 'F12', 'F4': 'F15', 'F7': 'F14', 'F10': 'F3', 'F15': 'F4', 'F3': 'F13', 'F13': 'F8', 'F16': 'F2', 'F2': 'F11', 'F11': 'F16', 'F8': 'F5', 'F14': 'F10', 'F1': 'F9', 'F12': 'F6', 'F5': 'F7'} | {'C1': 'C1', 'C2': 'C2'} | Satisfied | {'C1': 'Dissatisfied', 'C2': 'Satisfied'} |
LogisticRegression | C1 | Flight Price-Range Classification | Mainly based on the information on the case given, the classifier's output decision is as follows: C1 is the most probable label, followed by C3 and C2, with C2 being the least. To be specific, the prediction probabilities across the classes are as follows: C2 has 4.34%, C3 has 21.64%, and C1 has 74.0% chance of being ... | [
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"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
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] | 267 | 175 | {'C2': '4.34%', 'C3': '21.64%', 'C1': '74.02%'} | [
"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",
"F1",
"F9",
"F12",
"F8",
"F11",
"F5",
"F7",
"F10",
"F6",
"F4",
"F2"
] | {'F3': 'Airline', 'F1': 'Total_Stops', 'F9': 'Source', 'F12': 'Arrival_minute', 'F8': 'Arrival_hour', 'F11': 'Dep_minute', 'F5': 'Duration_hours', 'F7': 'Journey_month', 'F10': 'Journey_day', 'F6': 'Duration_mins', 'F4': 'Destination', 'F2': 'Dep_hour'} | {'F9': 'F3', 'F12': 'F1', 'F10': 'F9', 'F6': 'F12', 'F5': 'F8', 'F4': 'F11', 'F7': 'F5', 'F2': 'F7', 'F1': 'F10', 'F8': 'F6', 'F11': 'F4', 'F3': 'F2'} | {'C1': 'C2', 'C2': 'C3', 'C3': 'C1'} | High | {'C2': 'Low', 'C3': 'Moderate', 'C1': 'High'} |
LogisticRegression | C1 | Used Cars Price-Range Prediction | The model predicted C1 for the case under consideration which a predicted likelihood of 67.95% whereas, that of C2 is 32.05%. The top influencing features ordered from highest to lowest, are F9, F8, F6 and F1, and among them only F6 is shown to have positive attribution in support of the model's decision. F5, F10, and ... | [
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"negative",
"positive",
"negative",
"negative",
"positive",
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"negative",
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] | 20 | 7 | {'C2': '32.05%', 'C1': '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... | [
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"F8",
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"F2",
"F5",
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] | {'F9': 'Fuel_Type', 'F8': 'Seats', 'F6': 'car_age', 'F1': 'Name', 'F2': 'Owner_Type', 'F5': 'Power', 'F3': 'Engine', 'F4': 'Transmission', 'F10': 'Mileage', 'F7': 'Kilometers_Driven'} | {'F7': 'F9', 'F10': 'F8', 'F5': 'F6', 'F6': 'F1', 'F9': 'F2', 'F4': 'F5', 'F3': 'F3', 'F8': 'F4', 'F2': 'F10', 'F1': 'F7'} | {'C1': 'C2', 'C2': 'C1'} | High | {'C2': 'Low', 'C1': 'High'} |
LogisticRegression | C1 | Vehicle Insurance Claims | The case under consideration is labelled as C1 by the model employed for this classification problem. However, according to the model, there is a 45.34% chance that C2 could be the label, presenting some level of uncertainty in the classification verdict made here. F7, F15, F8, F16, F2, and F1 are the top features iden... | [
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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: F7 and F15.",
"Summarize the... | [
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"F29",
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] | {'F7': 'insured_hobbies', 'F15': 'incident_severity', 'F8': 'auto_make', 'F16': 'number_of_vehicles_involved', 'F1': 'insured_education_level', 'F2': 'collision_type', 'F5': 'insured_occupation', 'F21': 'incident_city', 'F28': 'incident_type', 'F23': 'auto_year', 'F9': 'insured_relationship', 'F20': 'authorities_contac... | {'F23': 'F7', 'F27': 'F15', 'F33': 'F8', 'F10': 'F16', 'F21': 'F1', 'F26': 'F2', 'F22': 'F5', 'F30': 'F21', 'F25': 'F28', 'F17': 'F23', 'F24': 'F9', 'F28': 'F20', 'F12': 'F17', 'F11': 'F26', 'F5': 'F19', 'F19': 'F30', 'F20': 'F24', 'F14': 'F3', 'F3': 'F27', 'F13': 'F14', 'F32': 'F10', 'F31': 'F33', 'F29': 'F25', 'F4': ... | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
GradientBoostingClassifier | C2 | Food Ordering Customer Churn Prediction | With a certainty level of 82.07 percent, the label choice for the given case is C2 and in a nutshell, the likelihood of C1 having the correct label is only 17.93%. The contributions of features like F30, F33, F38, and F36 are largely responsible for the classification above. The following three, with modest impact, are... | [
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"neg... | 258 | 335 | {'C1': '17.93%', 'C2': '82.07%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
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... | {'F30': 'More restaurant choices', 'F33': 'Ease and convenient', 'F38': 'Bad past experience', 'F36': 'Time saving', 'F23': 'Easy Payment option', 'F28': 'Good Tracking system', 'F5': 'Wrong order delivered', 'F10': 'Influence of rating', 'F12': 'Late Delivery', 'F40': 'Less Delivery time', 'F15': 'Long delivery time',... | {'F12': 'F30', 'F10': 'F33', 'F21': 'F38', 'F11': 'F36', 'F13': 'F23', 'F16': 'F28', 'F27': 'F5', 'F38': 'F10', 'F19': 'F12', 'F39': 'F40', 'F24': 'F15', 'F37': 'F45', 'F29': 'F43', 'F14': 'F46', 'F43': 'F26', 'F22': 'F1', 'F26': 'F19', 'F20': 'F11', 'F31': 'F13', 'F25': 'F22', 'F40': 'F39', 'F33': 'F35', 'F45': 'F20',... | {'C1': 'C1', 'C2': 'C2'} | Go Away | {'C1': 'Return', 'C2': 'Go Away'} |
DNN | C2 | Concrete Strength Classification | C2 was predicted with a high degree of certainty by the model since the likelihood of the alternative class is only 11.30%. The value of F8 has a significant negative impact on the classification choice, whereas F6 has a moderately positive contribution. F4, F3, F7, and F1 all have a favourable or positive impact on t... | [
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] | [
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
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] | 17 | 211 | {'C2': '88.70%', 'C1': '11.30%'} | [
"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: F8 and F6.",
"Compare and contrast the impact of the following features (F1, F7, F3 and F4) on the model’s prediction of C2.",
"Describe the... | [
"F8",
"F6",
"F1",
"F7",
"F3",
"F4",
"F2",
"F5"
] | {'F8': 'coarseaggregate', 'F6': 'age_days', 'F1': 'superplasticizer', 'F7': 'cement', 'F3': 'water', 'F4': 'fineaggregate', 'F2': 'slag', 'F5': 'flyash'} | {'F6': 'F8', 'F8': 'F6', 'F5': 'F1', 'F1': 'F7', 'F4': 'F3', 'F7': 'F4', 'F2': 'F2', 'F3': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Weak | {'C2': 'Weak', 'C1': 'Strong'} |
SVC | C2 | Broadband Sevice Signup | With a higher level of certainty, the algorithm labels the given data or case as C2 because the predicted probability of class C2 is 99.93% while that of class C1 is only 0.07%. C1 is therefore less likely than C2 and the classification assertion or decision here is chiefly attributed to the impact of input features su... | [
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"neg... | 235 | 141 | {'C2': '99.93%', 'C1': '0.07%'} | [
"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 and F6.",
"Compare and contrast the impact of the following features (F39, F35, F32 and F3) on the model’s prediction of C2.",
"Describe... | [
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"... | {'F16': 'X38', 'F6': 'X32', 'F39': 'X31', 'F35': 'X25', 'F32': 'X8', 'F3': 'X35', 'F33': 'X1', 'F24': 'X3', 'F21': 'X28', 'F4': 'X19', 'F22': 'X9', 'F10': 'X11', 'F19': 'X10', 'F37': 'X21', 'F12': 'X17', 'F38': 'X4', 'F5': 'X36', 'F8': 'X2', 'F11': 'X6', 'F29': 'X34', 'F27': 'X37', 'F1': 'X40', 'F30': 'X42', 'F9': 'X41... | {'F35': 'F16', 'F29': 'F6', 'F28': 'F39', 'F23': 'F35', 'F6': 'F32', 'F32': 'F3', 'F40': 'F33', 'F2': 'F24', 'F26': 'F21', 'F17': 'F4', 'F7': 'F22', 'F9': 'F10', 'F8': 'F19', 'F19': 'F37', 'F15': 'F12', 'F3': 'F38', 'F33': 'F5', 'F1': 'F8', 'F4': 'F11', 'F31': 'F29', 'F34': 'F27', 'F37': 'F1', 'F38': 'F30', 'F39': 'F9'... | {'C1': 'C2', 'C2': 'C1'} | No | {'C2': 'No', 'C1': 'Yes'} |
KNeighborsClassifier | C2 | E-Commerce Shipping | There is uncertainty about the correct label for the given example since both labels, C2 and C1 are shown to have a 50.0% chance of being correct. The prediction decision above is mainly attributed to the influence of the input features F1, F8, and F6, while F4, F2, and F5 are deemed less important to the decision abov... | [
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"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
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] | 203 | 118 | {'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... | [
"F1",
"F8",
"F6",
"F3",
"F10",
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"F4",
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] | {'F1': 'Discount_offered', 'F8': 'Weight_in_gms', 'F6': 'Prior_purchases', 'F3': 'Customer_care_calls', 'F10': 'Product_importance', 'F9': 'Mode_of_Shipment', 'F7': 'Warehouse_block', 'F4': 'Cost_of_the_Product', 'F2': 'Customer_rating', 'F5': 'Gender'} | {'F2': 'F1', 'F3': 'F8', 'F8': 'F6', 'F6': 'F3', 'F9': 'F10', 'F5': 'F9', 'F4': 'F7', 'F1': 'F4', 'F7': 'F2', 'F10': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | On-time | {'C2': 'On-time', 'C1': 'Late'} |
DecisionTreeClassifier | C2 | Credit Risk Classification | The classification model assigned the label C2 to the given example and given that the confidence level is 100.0%, we can be certain that the chances of C1 being the true label are negligible. The most relevant features controlling the prediction decision above are F4, F5, and F3. F9, F6, and F1 are among the least rel... | [
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"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative",
"positive",
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] | 131 | 277 | {'C1': '0.00%', 'C2': '100.00%'} | [
"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... | [
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"F5",
"F3",
"F11",
"F7",
"F2",
"F8",
"F10",
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] | {'F4': 'fea_4', 'F5': 'fea_8', 'F3': 'fea_5', 'F11': 'fea_2', 'F7': 'fea_1', 'F2': 'fea_9', 'F8': 'fea_11', 'F10': 'fea_6', 'F9': 'fea_10', 'F6': 'fea_7', 'F1': 'fea_3'} | {'F4': 'F4', 'F8': 'F5', 'F5': 'F3', 'F2': 'F11', 'F1': 'F7', 'F9': 'F2', 'F11': 'F8', 'F6': 'F10', 'F10': 'F9', 'F7': 'F6', 'F3': 'F1'} | {'C1': 'C1', 'C2': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
LogisticRegression | C1 | Hotel Satisfaction | The model prediction for the test case is C1 and the confidence level of this is almost 100%. From examining the contributions of variables or attributes, the values of F6 and F13 push the prediction verdict in favor of the other label. On the contrary, F10, F14, F4, and F8 have values with a positive influence that bi... | [
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"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive"
] | 144 | 208 | {'C1': '91.36%', 'C2': '8.64%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F6 (value equal to V0) and F13 (with a value equal to V0).",
"Compare and contrast the impact of the following features (F10, F14, F4 and F8... | [
"F6",
"F13",
"F10",
"F14",
"F4",
"F8",
"F5",
"F15",
"F12",
"F2",
"F7",
"F1",
"F11",
"F3",
"F9"
] | {'F6': 'Type of Travel', 'F13': 'Type Of Booking', 'F10': 'Hotel wifi service', 'F14': 'Common Room entertainment', 'F4': 'Stay comfort', 'F8': 'Other service', 'F5': 'Checkin\\/Checkout service', 'F15': 'Hotel location', 'F12': 'Food and drink', 'F2': 'Cleanliness', 'F7': 'Age', 'F1': 'Departure\\/Arrival convenience... | {'F3': 'F6', 'F4': 'F13', 'F6': 'F10', 'F12': 'F14', 'F11': 'F4', 'F14': 'F8', 'F13': 'F5', 'F9': 'F15', 'F10': 'F12', 'F15': 'F2', 'F5': 'F7', 'F7': 'F1', 'F2': 'F11', 'F8': 'F3', 'F1': 'F9'} | {'C1': 'C1', 'C2': 'C2'} | dissatisfied | {'C1': 'dissatisfied', 'C2': 'satisfied'} |
SGDClassifier | C1 | Company Bankruptcy Prediction | The label predicted by the classifier is C1 at a 71.80% confidence level. On the other hand, there is a 28.20% chance that C2 could be the label. The prediction can be mainly attributed to contributions from F38, F59, F47, and F27. Considerable positive contributions to the prediction here are from F38, F27, F52, and F... | [
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"neg... | 137 | 68 | {'C1': '71.80%', 'C2': '28.20%'} | [
"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: F38, F59, F47 and F27.",
"Compare and contrast the impact of the following features (F16, F52 and F75) on the model’s prediction of C1.",
"D... | [
"F38",
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"F50... | {'F38': ' Liability to Equity', 'F59': ' Net worth\\/Assets', 'F47': ' Debt ratio %', 'F27': ' Equity to Liability', 'F16': ' Realized Sales Gross Margin', 'F52': ' Net Value Per Share (A)', 'F75': ' Net Income to Total Assets', 'F13': ' Current Liability to Equity', 'F68': ' Current Liability to Assets', 'F32': ' Curr... | {'F66': 'F38', 'F84': 'F59', 'F47': 'F47', 'F91': 'F27', 'F83': 'F16', 'F42': 'F52', 'F16': 'F75', 'F92': 'F13', 'F46': 'F68', 'F39': 'F32', 'F6': 'F57', 'F88': 'F25', 'F67': 'F10', 'F87': 'F53', 'F44': 'F61', 'F86': 'F21', 'F71': 'F76', 'F54': 'F64', 'F7': 'F31', 'F80': 'F26', 'F57': 'F93', 'F58': 'F65', 'F59': 'F6', ... | {'C1': 'C1', 'C2': 'C2'} | No | {'C1': 'No', 'C2': 'Yes'} |
BernoulliNB | C2 | Employee Promotion Prediction | The model, making a classification decision based on the input variables, predicts the class C2 label for this case with a predicted likelihood equal to 54.21%. It also shows a 45.79% probability that C1 is the correct label. The classification decision made above is primarily influenced by the variables F2, F4, F1, F8... | [
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] | [
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"negative",
"positive",
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] | 157 | 223 | {'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... | [
"F2",
"F4",
"F8",
"F10",
"F1",
"F11",
"F9",
"F3",
"F7",
"F5",
"F6"
] | {'F2': 'KPIs_met >80%', 'F4': 'previous_year_rating', 'F8': 'avg_training_score', 'F10': 'department', 'F1': 'education', 'F11': 'recruitment_channel', 'F9': 'no_of_trainings', 'F3': 'length_of_service', 'F7': 'region', 'F5': 'age', 'F6': 'gender'} | {'F10': 'F2', 'F8': 'F4', 'F11': 'F8', 'F1': 'F10', 'F3': 'F1', 'F5': 'F11', 'F6': 'F9', 'F9': 'F3', 'F2': 'F7', 'F7': 'F5', 'F4': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | Ignore | {'C2': 'Ignore', 'C1': 'Promote'} |
MLPClassifier | C2 | Vehicle Insurance Claims | Based on the values of the input features, the classifier believes that the most probable label for the given data is C2, due to the fact that there is only a 19.30% chance that it could be C1 instead. The most influential features resulting in the decision or judgement above are F9, F24, F14, F6, F3, F30, and F22, tho... | [
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"neg... | 28 | 383 | {'C1': '19.30%', 'C2': '80.70%'} | [
"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",
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"F6",
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"F7",
"F4",
"F2",
"F1",
"F27",
"F32",
"F26",
"F17",
"F10"
] | {'F9': 'incident_severity', 'F24': 'insured_relationship', 'F6': 'authorities_contacted', 'F14': 'vehicle_claim', 'F30': 'umbrella_limit', 'F22': 'insured_hobbies', 'F3': 'incident_type', 'F11': 'policy_deductable', 'F18': 'auto_make', 'F23': 'number_of_vehicles_involved', 'F33': 'insured_occupation', 'F16': 'property_... | {'F27': 'F9', 'F24': 'F24', 'F28': 'F6', 'F16': 'F14', 'F5': 'F30', 'F23': 'F22', 'F25': 'F3', 'F3': 'F11', 'F33': 'F18', 'F10': 'F23', 'F22': 'F33', 'F31': 'F16', 'F29': 'F29', 'F17': 'F20', 'F8': 'F15', 'F19': 'F28', 'F26': 'F25', 'F7': 'F13', 'F15': 'F21', 'F9': 'F8', 'F32': 'F12', 'F4': 'F31', 'F30': 'F19', 'F6': '... | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
SGDClassifier | C2 | House Price Classification | According to the classification algorithm with a very high confidence level, the correct label for the given data instance is C2. This prediction decision is heavily influenced by features such as F1, F6, F2, F10, F13, and F9. Among these top features, the only features with a negative contribution towards the assigne... | [
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] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
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"positive",
"negative"
] | 218 | 129 | {'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... | [
"F1",
"F6",
"F2",
"F10",
"F9",
"F13",
"F5",
"F3",
"F7",
"F11",
"F12",
"F8",
"F4"
] | {'F1': 'AGE', 'F6': 'RAD', 'F2': 'LSTAT', 'F10': 'RM', 'F9': 'DIS', 'F13': 'CHAS', 'F5': 'ZN', 'F3': 'CRIM', 'F7': 'TAX', 'F11': 'B', 'F12': 'PTRATIO', 'F8': 'INDUS', 'F4': 'NOX'} | {'F7': 'F1', 'F9': 'F6', 'F13': 'F2', 'F6': 'F10', 'F8': 'F9', 'F4': 'F13', 'F2': 'F5', 'F1': 'F3', 'F10': 'F7', 'F12': 'F11', 'F11': 'F12', 'F3': 'F8', 'F5': 'F4'} | {'C1': 'C1', 'C2': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
RandomForestClassifier | C2 | Bike Sharing Demand | For the given data instance, the most probable class according to the classifier is C2 since the probability of C1 being the correct label is only about 10.0%. The most influential features resulting in the prediction decision above are F8, F1, and F7 which are shown to negatively contribute to the decision above sinc... | [
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] | [
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 219 | 130 | {'C2': '90.00%', 'C1': '10.00%'} | [
"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",
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"F10"
] | {'F8': 'Functioning Day', 'F1': 'Rainfall(mm)', 'F7': 'Temperature', 'F5': 'Solar Radiation (MJ\\/m2)', 'F9': 'Seasons', 'F12': 'Wind speed (m\\/s)', 'F4': 'Holiday', 'F3': 'Visibility (10m)', 'F6': 'Dew point temperature', 'F11': 'Hour', 'F2': 'Snowfall (cm)', 'F10': 'Humidity(%)'} | {'F12': 'F8', 'F8': 'F1', 'F2': 'F7', 'F7': 'F5', 'F10': 'F9', 'F4': 'F12', 'F11': 'F4', 'F5': 'F3', 'F6': 'F6', 'F1': 'F11', 'F9': 'F2', 'F3': 'F10'} | {'C1': 'C2', 'C2': 'C1'} | Less than 500 | {'C2': 'Less than 500', 'C1': 'More than 500'} |
RandomForestClassifier | C1 | Advertisement Prediction | Judging based on the information about the given case, the model outputs C1 with a prediction probability of 74.72%, however, it is vital to keep in mind that there is also a 25.28% probability that C2 could be the true label. The attribution analysis shows that all the input variables have varying degrees of influence... | [
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] | [
"positive",
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"negative",
"positive",
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] | 31 | 386 | {'C1': '74.72%', 'C2': '25.28%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F5 and F1.",
"Compare and contrast the impact of the following features (F3, F7 (when it is equal to V1), F6 and F4 (when it is equal to V1)... | [
"F5",
"F1",
"F3",
"F7",
"F6",
"F4",
"F2"
] | {'F5': 'Daily Time Spent on Site', 'F1': 'Daily Internet Usage', 'F3': 'Age', 'F7': 'ad_day', 'F6': 'Area Income', 'F4': 'Gender', 'F2': 'ad_month'} | {'F1': 'F5', 'F4': 'F1', 'F2': 'F3', 'F7': 'F7', 'F3': 'F6', 'F5': 'F4', 'F6': 'F2'} | {'C1': 'C1', 'C2': 'C2'} | Skip | {'C1': 'Skip', 'C2': 'Watch'} |
DNN | C2 | Credit Card Fraud Classification | The data is labelled C2 by the model as it has a somewhat greater prediction chance than C1. F6, F5, F10, F13, and F28 are the input variables that have the most impact on the above classification choice, whereas F14, F19, F30, F9, and F12 have the least influence. F6, F5, F28, and F13 are basically supporting the choi... | [
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"positiv... | 241 | 320 | {'C1': '48.58%', 'C2': '51.42%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F5",
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] | {'F5': 'Z18', 'F6': 'Z14', 'F10': 'Time', 'F13': 'Z1', 'F28': 'Z19', 'F3': 'Z10', 'F25': 'Z4', 'F22': 'Z3', 'F23': 'Z12', 'F7': 'Z16', 'F18': 'Z7', 'F1': 'Z11', 'F26': 'Z9', 'F8': 'Z6', 'F2': 'Z23', 'F4': 'Z5', 'F15': 'Z17', 'F29': 'Z21', 'F20': 'Z24', 'F21': 'Z8', 'F24': 'Amount', 'F27': 'Z20', 'F16': 'Z27', 'F11': 'Z... | {'F19': 'F5', 'F15': 'F6', 'F1': 'F10', 'F2': 'F13', 'F20': 'F28', 'F11': 'F3', 'F5': 'F25', 'F4': 'F22', 'F13': 'F23', 'F17': 'F7', 'F8': 'F18', 'F12': 'F1', 'F10': 'F26', 'F7': 'F8', 'F24': 'F2', 'F6': 'F4', 'F18': 'F15', 'F22': 'F29', 'F25': 'F20', 'F9': 'F21', 'F30': 'F24', 'F21': 'F27', 'F28': 'F16', 'F26': 'F11',... | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
GradientBoostingClassifier | C1 | Printer Sales | Although the case under consideration has variables with a significant negative impact, it also has many measurable variables that are positive, so there is a good chance that C1 is correct since it has a 91.95% certainty. F15, F2, and F6 are the most important input variables, thanks to which the model successfully as... | [
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"neg... | 111 | 239 | {'C2': '8.05%', 'C1': '91.95%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F19, F22 and F4) on the model’s prediction of C1.",
"Summarize the set of ... | [
"F15",
"F2",
"F6",
"F9",
"F18",
"F19",
"F22",
"F4",
"F8",
"F10",
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"F24",
"F14",
"F21",
"F20",
"F13",
"F16",
"F1",
"F26",
"F17",
"F5"
] | {'F15': 'X24', 'F2': 'X8', 'F6': 'X1', 'F9': 'X21', 'F18': 'X4', 'F19': 'X6', 'F22': 'X3', 'F4': 'X22', 'F8': 'X7', 'F10': 'X15', 'F11': 'X20', 'F7': 'X11', 'F23': 'X10', 'F25': 'X19', 'F3': 'X5', 'F12': 'X16', 'F24': 'X23', 'F14': 'X9', 'F21': 'X17', 'F20': 'X18', 'F13': 'X25', 'F16': 'X14', 'F1': 'X2', 'F26': 'X13', ... | {'F24': 'F15', 'F8': 'F2', 'F1': 'F6', 'F21': 'F9', 'F4': 'F18', 'F6': 'F19', 'F3': 'F22', 'F22': 'F4', 'F7': 'F8', 'F15': 'F10', 'F20': 'F11', 'F11': 'F7', 'F10': 'F23', 'F19': 'F25', 'F5': 'F3', 'F16': 'F12', 'F23': 'F24', 'F9': 'F14', 'F17': 'F21', 'F18': 'F20', 'F25': 'F13', 'F14': 'F16', 'F2': 'F1', 'F13': 'F26', ... | {'C1': 'C2', 'C2': 'C1'} | More | {'C2': 'Less', 'C1': 'More'} |
LogisticRegression | C3 | Air Quality Prediction | The classification output observations that follow are based on the information supplied about this specific case. The class label in this case is forecasted to be C3 out of the four possible labels, with a probability of around 83.08 percent. With a probability of 16.87 percent, C2 is the next most likely label. The t... | [
"-0.27",
"0.16",
"0.12",
"-0.04",
"0.03",
"0.01"
] | [
"negative",
"positive",
"positive",
"negative",
"positive",
"positive"
] | 55 | 414 | {'C3': '83.08%', 'C2': '16.87%', 'C1': '0.00%', 'C4': '0.05%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F4, F3 and F1) on the prediction made for this test case.",
"Compare the direction of impact of the features: F5, F6 and F2.",
"Describe the degree of impa... | [
"F4",
"F3",
"F1",
"F5",
"F6",
"F2"
] | {'F4': 'MQ5', 'F3': 'MQ3', 'F1': 'MQ1', 'F5': 'MQ4', 'F6': 'MQ6', 'F2': 'MQ2'} | {'F5': 'F4', 'F3': 'F3', 'F1': 'F1', 'F4': 'F5', 'F6': 'F6', 'F2': 'F2'} | {'C1': 'C3', 'C2': 'C2', 'C3': 'C1', 'C4': 'C4'} | Preparing meals | {'C3': 'Preparing meals', 'C2': 'Presence of smoke', 'C1': 'Cleaning', 'C4': 'Other'} |
DecisionTreeClassifier | C2 | Insurance Churn | The model predicted class C2 with a very high confidence level of 93.27% and looking at the predicted probabilities across the label, there is only a 6.73% chance that C1 is the true label. There are two features that have a very strong positive effect on the prediction of class C2 and these are F12 and F4. The followi... | [
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"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 83 | 285 | {'C1': '6.73%', 'C2': '93.27%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F12",
"F4",
"F16",
"F9",
"F3",
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"F5",
"F10",
"F2",
"F7",
"F13",
"F15",
"F14",
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] | {'F12': 'feature15', 'F4': 'feature14', 'F16': 'feature10', 'F9': 'feature11', 'F3': 'feature5', 'F8': 'feature13', 'F5': 'feature4', 'F10': 'feature3', 'F2': 'feature12', 'F7': 'feature1', 'F13': 'feature7', 'F15': 'feature2', 'F14': 'feature6', 'F1': 'feature0', 'F11': 'feature9', 'F6': 'feature8'} | {'F9': 'F12', 'F8': 'F4', 'F4': 'F16', 'F5': 'F9', 'F15': 'F3', 'F7': 'F8', 'F14': 'F5', 'F13': 'F10', 'F6': 'F2', 'F11': 'F7', 'F1': 'F13', 'F12': 'F15', 'F16': 'F14', 'F10': 'F1', 'F3': 'F11', 'F2': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | Leave | {'C1': 'Stay', 'C2': 'Leave'} |
RandomForestClassifier | C3 | Flight Price-Range Classification | Between the three possible classes, there is a 100% certainty that the correct label for this case is C3. The features with a very high impact on the prediction made here are F7, F6, and F2, which are also shown to have a very strong positive contribution to the C3 prediction. Other features that shift the prediction i... | [
"0.23",
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"0.17",
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"0.01",
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"-0.01",
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] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive"
] | 114 | 51 | {'C3': '100.00%', 'C1': '0.00%', 'C2': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features... | [
"F7",
"F2",
"F6",
"F10",
"F5",
"F11",
"F3",
"F12",
"F8",
"F4",
"F9",
"F1"
] | {'F7': 'Duration_hours', 'F2': 'Airline', 'F6': 'Total_Stops', 'F10': 'Journey_day', 'F5': 'Source', 'F11': 'Destination', 'F3': 'Journey_month', 'F12': 'Dep_minute', 'F8': 'Arrival_minute', 'F4': 'Arrival_hour', 'F9': 'Duration_mins', 'F1': 'Dep_hour'} | {'F7': 'F7', 'F9': 'F2', 'F12': 'F6', 'F1': 'F10', 'F10': 'F5', 'F11': 'F11', 'F2': 'F3', 'F4': 'F12', 'F6': 'F8', 'F5': 'F4', 'F8': 'F9', 'F3': 'F1'} | {'C1': 'C3', 'C2': 'C1', 'C3': 'C2'} | Low | {'C3': 'Low', 'C1': 'Moderate', 'C2': 'High'} |
SVC | C2 | Advertisement Prediction | For the given case or instance, the model assigns the label C2, with the prediction confidence equal to 56.56%. The variables F4, F1, F5, and F6 all contribute a lot to the classification decision above. While F4 and F5 are impacting positively, F1 and F6 are decreasing the likelihood of the assigned label. For the rem... | [
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] | [
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative"
] | 45 | 13 | {'C2': '56.56%', 'C1': '43.44%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F4, F1 and F5) on the prediction made for this test case.",
"Compare the direction of impact of the features: F6 (with a value equal to V4), F3 (when it is ... | [
"F4",
"F1",
"F5",
"F6",
"F3",
"F7",
"F2"
] | {'F4': 'Daily Time Spent on Site', 'F1': 'Daily Internet Usage', 'F5': 'Age', 'F6': 'ad_day', 'F3': 'ad_month', 'F7': 'Area Income', 'F2': 'Gender'} | {'F1': 'F4', 'F4': 'F1', 'F2': 'F5', 'F7': 'F6', 'F6': 'F3', 'F3': 'F7', 'F5': 'F2'} | {'C1': 'C2', 'C2': 'C1'} | Skip | {'C2': 'Skip', 'C1': 'Watch'} |
RandomForestClassifier | C1 | Paris House Classification | The prediction made for this case by the model is that C1 is most likely the true label, with a confidence level of 72.03% higher than the 27.97% of the C2 label. According to the input features attribution analysis conducted, the features with the most influence on the decision are F1, F8, F15, and F10, all of which i... | [
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"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
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] | 151 | 434 | {'C2': '27.97%', 'C1': '72.03%'} | [
"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, F1 and F15) on the prediction made for this test case.",
"Compare the direction of impact of the features: F10, F11 and F17.",
"Describe the degree of ... | [
"F8",
"F1",
"F15",
"F10",
"F11",
"F17",
"F5",
"F9",
"F6",
"F4",
"F7",
"F12",
"F3",
"F2",
"F13",
"F14",
"F16"
] | {'F8': 'isNewBuilt', 'F1': 'hasYard', 'F15': 'hasPool', 'F10': 'hasStormProtector', 'F11': 'made', 'F17': 'hasGuestRoom', 'F5': 'floors', 'F9': 'squareMeters', 'F6': 'numPrevOwners', 'F4': 'cityCode', 'F7': 'price', 'F12': 'numberOfRooms', 'F3': 'basement', 'F2': 'attic', 'F13': 'cityPartRange', 'F14': 'hasStorageRoom'... | {'F3': 'F8', 'F1': 'F1', 'F2': 'F15', 'F4': 'F10', 'F12': 'F11', 'F16': 'F17', 'F8': 'F5', 'F6': 'F9', 'F11': 'F6', 'F9': 'F4', 'F17': 'F7', 'F7': 'F12', 'F13': 'F3', 'F14': 'F2', 'F10': 'F13', 'F5': 'F14', 'F15': 'F16'} | {'C1': 'C2', 'C2': 'C1'} | Luxury | {'C2': 'Basic', 'C1': 'Luxury'} |
LogisticRegression | C1 | Annual Income Earnings | Tasked with labelling cases, the classification model labels the case under consideration as C1 since the probability of C2 is only 20.22%. The predicted probability of the less probable class, C2, reflects the fact that the model is a bit doubtful about the output label. Responsible for this doubt are the negative fea... | [
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"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
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] | 40 | 395 | {'C2': '20.22%', 'C1': '79.78%'} | [
"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, F5 (equal to V2), F3 (when it is equal to V12), F14 and F12) on the prediction made for this test case.",
"Compare the direction of impact of the feat... | [
"F10",
"F5",
"F3",
"F14",
"F12",
"F8",
"F6",
"F1",
"F4",
"F13",
"F11",
"F9",
"F2",
"F7"
] | {'F10': 'Capital Gain', 'F5': 'Marital Status', 'F3': 'Education', 'F14': 'Capital Loss', 'F12': 'Hours per week', 'F8': 'Sex', 'F6': 'Country', 'F1': 'Education-Num', 'F4': 'Occupation', 'F13': 'Race', 'F11': 'Age', 'F9': 'Workclass', 'F2': 'fnlwgt', 'F7': 'Relationship'} | {'F11': 'F10', 'F6': 'F5', 'F4': 'F3', 'F12': 'F14', 'F13': 'F12', 'F10': 'F8', 'F14': 'F6', 'F5': 'F1', 'F7': 'F4', 'F9': 'F13', 'F1': 'F11', 'F2': 'F9', 'F3': 'F2', 'F8': 'F7'} | {'C1': 'C2', 'C2': 'C1'} | Above 50K | {'C2': 'Under 50K', 'C1': 'Above 50K'} |
SVM_poly | C1 | Mobile Price-Range Classification | The classification algorithm determines that neither C3 nor C4 nor C2 is a suitable label for the present context. C1 is quite guaranteed to be the correct label. The aforementioned conclusion has a higher degree of confidence due to the positive contributions of F18, F20, and F11. Aside from the above mentioned positi... | [
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"negative",
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"negative",
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"negative",
"positive",
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] | 251 | 342 | {'C3': '0.00%', 'C4': '0.00%', '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... | [
"F18",
"F20",
"F11",
"F13",
"F16",
"F19",
"F14",
"F1",
"F15",
"F4",
"F2",
"F12",
"F8",
"F17",
"F10",
"F6",
"F5",
"F7",
"F9",
"F3"
] | {'F18': 'ram', 'F20': 'battery_power', 'F11': 'px_width', 'F13': 'int_memory', 'F16': 'sc_h', 'F19': 'wifi', 'F14': 'fc', 'F1': 'three_g', 'F15': 'mobile_wt', 'F4': 'clock_speed', 'F2': 'm_dep', 'F12': 'n_cores', 'F8': 'pc', 'F17': 'touch_screen', 'F10': 'blue', 'F6': 'talk_time', 'F5': 'sc_w', 'F7': 'px_height', 'F9':... | {'F11': 'F18', 'F1': 'F20', 'F10': 'F11', 'F4': 'F13', 'F12': 'F16', 'F20': 'F19', 'F3': 'F14', 'F18': 'F1', 'F6': 'F15', 'F2': 'F4', 'F5': 'F2', 'F7': 'F12', 'F8': 'F8', 'F19': 'F17', 'F15': 'F10', 'F14': 'F6', 'F13': 'F5', 'F9': 'F7', 'F17': 'F9', 'F16': 'F3'} | {'C1': 'C3', 'C2': 'C4', 'C3': 'C2', 'C4': 'C1'} | r4 | {'C3': 'r1', 'C4': 'r2', 'C2': 'r3', 'C1': 'r4'} |
SVM_linear | C1 | Employee Promotion Prediction | The model gave the output label as C1 with a very high probability of 99.69%, leaving only 0.31% chance that C2 could be the right one. According to the contributions or attributions analysis done to understand the properties of various traits, F10 is by far the most influential trait. F8 had a positive impact on model... | [
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"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive"
] | 100 | 241 | {'C2': '0.31%', 'C1': '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 (F8, F6 (with a value equal to V2), F4 and F3) on the model’s prediction of ... | [
"F10",
"F9",
"F8",
"F6",
"F4",
"F3",
"F11",
"F2",
"F5",
"F7",
"F1"
] | {'F10': 'avg_training_score', 'F9': 'department', 'F8': 'KPIs_met >80%', 'F6': 'recruitment_channel', 'F4': 'age', 'F3': 'no_of_trainings', 'F11': 'previous_year_rating', 'F2': 'education', 'F5': 'region', 'F7': 'length_of_service', 'F1': 'gender'} | {'F11': 'F10', 'F1': 'F9', 'F10': 'F8', 'F5': 'F6', 'F7': 'F4', 'F6': 'F3', 'F8': 'F11', 'F3': 'F2', 'F2': 'F5', 'F9': 'F7', 'F4': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Promote | {'C2': 'Ignore', 'C1': 'Promote'} |
RandomForestClassifier | C2 | Credit Risk Classification | Between the two classes, the given case is assigned the label C2 given that it has the highest predicted probability of about 93.0% since the probability of having C1 as the label is only 7.0%. Analysing the prediction made for the case under consideration, F6, F9, F4, and F10 are the features mainly pushing the predic... | [
"0.10",
"-0.02",
"0.01",
"-0.01",
"0.01",
"0.01",
"-0.00",
"-0.00",
"-0.00",
"-0.00",
"-0.00"
] | [
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 182 | 105 | {'C2': '93.00%', 'C1': '7.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of i... | [
"F5",
"F6",
"F8",
"F9",
"F1",
"F3",
"F4",
"F10",
"F7",
"F2",
"F11"
] | {'F5': 'fea_4', 'F6': 'fea_10', 'F8': 'fea_8', 'F9': 'fea_7', 'F1': 'fea_2', 'F3': 'fea_3', 'F4': 'fea_5', 'F10': 'fea_1', 'F7': 'fea_9', 'F2': 'fea_6', 'F11': 'fea_11'} | {'F4': 'F5', 'F10': 'F6', 'F8': 'F8', 'F7': 'F9', 'F2': 'F1', 'F3': 'F3', 'F5': 'F4', 'F1': 'F10', 'F9': 'F7', 'F6': 'F2', 'F11': 'F11'} | {'C1': 'C2', 'C2': 'C1'} | Low | {'C2': 'Low', 'C1': 'High'} |
SVM_linear | C1 | Wine Quality Prediction | The classifier says that C1 has a 67.54 percent chance of being the correct label for the given example or case; consequently the label C2 has a 33.46 percent chance of being the chosen class. The variables F2, F11, F9, and F1 have the most impact on the prediction judgement here. On the other hand, F8, F3, and F5 are ... | [
"0.09",
"0.08",
"0.06",
"-0.03",
"0.03",
"-0.01",
"0.01",
"0.01",
"0.01",
"-0.01",
"-0.00"
] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative"
] | 176 | 218 | {'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 (F2, F11, F9 and F1) on the prediction made for this test case.",
"Compare the direction of impact of the features: F6, F10 and F7.",
"Describe the degree o... | [
"F2",
"F11",
"F9",
"F1",
"F6",
"F10",
"F7",
"F4",
"F8",
"F3",
"F5"
] | {'F2': 'residual sugar', 'F11': 'volatile acidity', 'F9': 'alcohol', 'F1': 'fixed acidity', 'F6': 'chlorides', 'F10': 'sulphates', 'F7': 'citric acid', 'F4': 'free sulfur dioxide', 'F8': 'density', 'F3': 'total sulfur dioxide', 'F5': 'pH'} | {'F4': 'F2', 'F2': 'F11', 'F11': 'F9', 'F1': 'F1', 'F5': 'F6', 'F10': 'F10', 'F3': 'F7', 'F6': 'F4', 'F8': 'F8', 'F7': 'F3', 'F9': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | high quality | {'C2': 'low_quality', 'C1': 'high quality'} |
SVC | C2 | Australian Credit Approval | The classification algorithm labels the presented data as C2 with the degree of confidence equal to 81.43 percent, although there is an 18.57 percent possibility that C1 is the correct label. The positive effects and contributions of input variables F8, F12, and F9 are mostly used to assign C2 to a specific scenario. F... | [
"0.43",
"0.14",
"0.14",
"0.09",
"0.07",
"0.06",
"0.05",
"-0.04",
"0.04",
"-0.03",
"0.03",
"-0.03",
"0.02",
"-0.01"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 244 | 314 | {'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... | [
"F8",
"F12",
"F9",
"F13",
"F5",
"F7",
"F2",
"F6",
"F11",
"F3",
"F14",
"F10",
"F4",
"F1"
] | {'F8': 'A8', 'F12': 'A9', 'F9': 'A14', 'F13': 'A12', 'F5': 'A7', 'F7': 'A4', 'F2': 'A5', 'F6': 'A11', 'F11': 'A1', 'F3': 'A13', 'F14': 'A10', 'F10': 'A2', 'F4': 'A6', 'F1': 'A3'} | {'F8': 'F8', 'F9': 'F12', 'F14': 'F9', 'F12': 'F13', 'F7': 'F5', 'F4': 'F7', 'F5': 'F2', 'F11': 'F6', 'F1': 'F11', 'F13': 'F3', 'F10': 'F14', 'F2': 'F10', 'F6': 'F4', 'F3': 'F1'} | {'C1': 'C1', 'C2': 'C2'} | Class 2 | {'C1': 'Class 1', 'C2': 'Class 2'} |
RandomForestClassifier | C1 | E-Commerce Shipping | The probability that the label is C1 is 51.62% and the probability that C2 is the correct label is 48.38%. For this case or example, the uncertainty of the model is mainly due to the direction of influence of the variables F1, F7, and F9. Reducing the chance that C1 is the correct label are variables F1, F9, F3, and F6... | [
"-0.10",
"0.06",
"-0.02",
"-0.02",
"0.01",
"0.01",
"0.01",
"0.01",
"0.01",
"-0.00"
] | [
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative"
] | 163 | 222 | {'C1': '51.62%', 'C2': '48.38%'} | [
"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, F3, F2 and F10) on the model’s prediction of C1.",
"Summarize the set ... | [
"F1",
"F7",
"F9",
"F3",
"F2",
"F10",
"F4",
"F8",
"F5",
"F6"
] | {'F1': 'Discount_offered', 'F7': 'Weight_in_gms', 'F9': 'Customer_care_calls', 'F3': 'Product_importance', 'F2': 'Mode_of_Shipment', 'F10': 'Warehouse_block', 'F4': 'Cost_of_the_Product', 'F8': 'Gender', 'F5': 'Customer_rating', 'F6': 'Prior_purchases'} | {'F2': 'F1', 'F3': 'F7', 'F6': 'F9', 'F9': 'F3', 'F5': 'F2', 'F4': 'F10', 'F1': 'F4', 'F10': 'F8', 'F7': 'F5', 'F8': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | On-time | {'C1': 'On-time', 'C2': 'Late'} |
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