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Add proper confidence to XGBoost model prediction (closes #8)
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‎notebooks/Counterfactuals.ipynb‎

Lines changed: 93 additions & 88 deletions
Original file line numberDiff line numberDiff line change
@@ -20,8 +20,6 @@
2020
"trustyai.init(\n",
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" path=[\n",
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" \"../dep/org/kie/kogito/explainability-core/1.8.0.Final/*\",\n",
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"# \"../dep/org/kie/kogito/explainability-core/1.8.0.Final/explainability-core-2.0.0-SNAPSHOT.jar\",\n",
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"# \"../dep/org/kie/kogito/explainability-core/1.8.0.Final/explainability-core-1.8.0.Final-tests.jar\",\n",
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" \"../dep/org/slf4j/slf4j-api/1.7.30/slf4j-api-1.7.30.jar\",\n",
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" \"../dep/org/apache/commons/commons-lang3/3.12.0/commons-lang3-3.12.0.jar\",\n",
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" \"../dep/org/optaplanner/optaplanner-core/8.8.0.Final/optaplanner-core-8.8.0.Final.jar\",\n",
@@ -141,12 +139,12 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Feature x1 has value 5.89410604979949\n",
145-
"Feature x2 has value 4.6702081567068525\n",
146-
"Feature x3 has value 7.484002107104038\n",
147-
"Feature x4 has value 8.39980161062865\n",
142+
"Feature x1 has value 9.135811031480424\n",
143+
"Feature x2 has value 5.0454318078238245\n",
144+
"Feature x3 has value 1.1416494951836265\n",
145+
"Feature x4 has value 0.362431844924509\n",
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"\n",
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"Features sum is 26.44811792423903\n"
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"Features sum is 15.685324179412383\n"
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]
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}
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],
@@ -378,12 +376,12 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"java.lang.DoubleFeature{value=6.143270159874259, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x1'}\n",
382-
"java.lang.DoubleFeature{value=4.6702081567068525, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x2'}\n",
383-
"java.lang.DoubleFeature{value=7.484002107104038, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x3'}\n",
384-
"java.lang.DoubleFeature{value=481.34447531284985, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x4'}\n",
379+
"java.lang.DoubleFeature{value=9.135811031480424, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x1'}\n",
380+
"java.lang.DoubleFeature{value=6.268698018465302, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x2'}\n",
381+
"java.lang.DoubleFeature{value=484.55755944660217, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x3'}\n",
382+
"java.lang.DoubleFeature{value=0.362431844924509, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x4'}\n",
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"\n",
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"Feature sum is 499.641955736535\n"
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"Feature sum is 500.3245003414724\n"
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]
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}
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],
@@ -472,13 +470,13 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Original x1: 5.89410604979949\n",
476-
"Original x4: 8.39980161062865\n",
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"Original x1: 9.135811031480424\n",
474+
"Original x4: 0.362431844924509\n",
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"\n",
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"java.lang.DoubleFeature{value=5.89410604979949, intRangeMinimum=5.89410604979949, intRangeMaximum=5.89410604979949, id='x1'}\n",
479-
"java.lang.DoubleFeature{value=4.467150339062309, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x2'}\n",
480-
"java.lang.DoubleFeature{value=480.4495023914547, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x3'}\n",
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"java.lang.DoubleFeature{value=8.39980161062865, intRangeMinimum=8.39980161062865, intRangeMaximum=8.39980161062865, id='x4'}\n"
476+
"java.lang.DoubleFeature{value=9.135811031480424, intRangeMinimum=9.135811031480424, intRangeMaximum=9.135811031480424, id='x1'}\n",
477+
"java.lang.DoubleFeature{value=5.522381982853708, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x2'}\n",
478+
"java.lang.DoubleFeature{value=484.5070395184806, intRangeMinimum=0.0, intRangeMaximum=1000.0, id='x3'}\n",
479+
"java.lang.DoubleFeature{value=0.362431844924509, intRangeMinimum=0.362431844924509, intRangeMaximum=0.362431844924509, id='x4'}\n"
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]
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}
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],
@@ -659,9 +657,13 @@
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"\n",
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"def predict(inputs: List[PredictionInput]) -> List[PredictionOutput]:\n",
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" values = [feature.getValue().asNumber() for feature in inputs.get(0).getFeatures()]\n",
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" result = xg_model.predict(np.array([values]))\n",
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" value = False if result[0]==0 else True\n",
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" output = Output(\"PaidLoan\", Type.BOOLEAN, Value(value), 0.0)\n",
660+
" result = xg_model.predict_proba(np.array([values]))\n",
661+
" false_prob, true_prob = result[0]\n",
662+
" if false_prob > true_prob:\n",
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" prediction = (False, false_prob)\n",
664+
" else:\n",
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" prediction = (True, true_prob)\n",
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" output = Output(\"PaidLoan\", Type.BOOLEAN, Value(prediction[0]), prediction[1])\n",
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" return toJList([PredictionOutput([output])])"
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]
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},
@@ -697,52 +699,56 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 27,
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"id": "06d52535",
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"metadata": {},
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"outputs": [],
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"source": [
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"features = [\n",
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" FeatureFactory.newBooleanFeature(\"NewCreditCustomer\", False),\n",
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" FeatureFactory.newNumericalFeature(\"Amount\", 2125.0),\n",
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" FeatureFactory.newNumericalFeature(\"Interest\", 20.97),\n",
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" FeatureFactory.newNumericalFeature(\"LoanDuration\", 60.0),\n",
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" FeatureFactory.newNumericalFeature(\"Education\", 4.0),\n",
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" FeatureFactory.newNumericalFeature(\"NrOfDependants\", 0.0),\n",
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" FeatureFactory.newNumericalFeature(\"EmploymentDurationCurrentEmployer\", 6.0),\n",
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" FeatureFactory.newNumericalFeature(\"IncomeFromPrincipalEmployer\", 0.0),\n",
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" FeatureFactory.newNumericalFeature(\"IncomeFromPension\", 301.0),\n",
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" FeatureFactory.newNumericalFeature(\"IncomeFromFamilyAllowance\", 0.0),\n",
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" FeatureFactory.newNumericalFeature(\"IncomeFromSocialWelfare\", 53.0),\n",
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" FeatureFactory.newNumericalFeature(\"IncomeFromLeavePay\", 0.0),\n",
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" FeatureFactory.newNumericalFeature(\"IncomeFromChildSupport\", 0.0),\n",
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" FeatureFactory.newNumericalFeature(\"IncomeOther\", 0.0),\n",
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" FeatureFactory.newNumericalFeature(\"ExistingLiabilities\", 8.0),\n",
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" FeatureFactory.newNumericalFeature(\"RefinanceLiabilities\", 6.0),\n",
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" FeatureFactory.newNumericalFeature(\"DebtToIncome\", 26.29),\n",
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" FeatureFactory.newNumericalFeature(\"FreeCash\", 10.92),\n",
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" FeatureFactory.newNumericalFeature(\"CreditScoreEeMini\", 1000.0),\n",
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" FeatureFactory.newNumericalFeature(\"NoOfPreviousLoansBeforeLoan\", 1.0),\n",
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" FeatureFactory.newNumericalFeature(\"AmountOfPreviousLoansBeforeLoan\", 500.0),\n",
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" FeatureFactory.newNumericalFeature(\"PreviousRepaymentsBeforeLoan\", 590.95),\n",
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" FeatureFactory.newNumericalFeature(\"PreviousEarlyRepaymentsBefoleLoan\", 0.0),\n",
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" FeatureFactory.newNumericalFeature(\"PreviousEarlyRepaymentsCountBeforeLoan\", 0.0),\n",
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" FeatureFactory.newBooleanFeature(\"Council_house\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Homeless\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Joint_ownership\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Joint_tenant\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Living_with_parents\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Mortgage\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Other\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Owner\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Owner_with_encumbrance\", True),\n",
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" FeatureFactory.newBooleanFeature(\"Tenant\", True),\n",
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" FeatureFactory.newBooleanFeature(\"Entrepreneur\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Fully\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Partially\", False),\n",
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" FeatureFactory.newBooleanFeature(\"Retiree\", True),\n",
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" FeatureFactory.newBooleanFeature(\"Self_employed\", False), \n",
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"]"
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"def make_feature(name, value):\n",
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" if type(value) is bool:\n",
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" return FeatureFactory.newBooleanFeature(name, value)\n",
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" else:\n",
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" return FeatureFactory.newNumericalFeature(name, value)\n",
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"\n",
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"features = [make_feature(p[0], p[1]) for p in [(\"NewCreditCustomer\", False),\n",
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" (\"Amount\", 2125.0),\n",
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" (\"Interest\", 20.97),\n",
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" (\"LoanDuration\", 60.0),\n",
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" (\"Education\", 4.0),\n",
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" (\"NrOfDependants\", 0.0),\n",
719+
" (\"EmploymentDurationCurrentEmployer\", 6.0),\n",
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" (\"IncomeFromPrincipalEmployer\", 0.0),\n",
721+
" (\"IncomeFromPension\", 301.0),\n",
722+
" (\"IncomeFromFamilyAllowance\", 0.0),\n",
723+
" (\"IncomeFromSocialWelfare\", 53.0),\n",
724+
" (\"IncomeFromLeavePay\", 0.0),\n",
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" (\"IncomeFromChildSupport\", 0.0),\n",
726+
" (\"IncomeOther\", 0.0),\n",
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" (\"ExistingLiabilities\", 8.0),\n",
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" (\"RefinanceLiabilities\", 6.0),\n",
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" (\"DebtToIncome\", 26.29),\n",
730+
" (\"FreeCash\", 10.92),\n",
731+
" (\"CreditScoreEeMini\", 1000.0),\n",
732+
" (\"NoOfPreviousLoansBeforeLoan\", 1.0),\n",
733+
" (\"AmountOfPreviousLoansBeforeLoan\", 500.0),\n",
734+
" (\"PreviousRepaymentsBeforeLoan\", 590.95),\n",
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" (\"PreviousEarlyRepaymentsBefoleLoan\", 0.0),\n",
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" (\"PreviousEarlyRepaymentsCountBeforeLoan\", 0.0),\n",
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" (\"Council_house\", False),\n",
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" (\"Homeless\", False),\n",
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" (\"Joint_ownership\", False),\n",
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" (\"Joint_tenant\", False),\n",
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" (\"Living_with_parents\", False),\n",
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" (\"Mortgage\", False),\n",
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" (\"Other\", False),\n",
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" (\"Owner\", False),\n",
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" (\"Owner_with_encumbrance\", True),\n",
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" (\"Tenant\", True),\n",
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" (\"Entrepreneur\", False),\n",
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" (\"Fully\", False),\n",
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" (\"Partially\", False),\n",
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" (\"Retiree\", True),\n",
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" (\"Self_employed\", False)]]"
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]
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},
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{
@@ -755,17 +761,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 28,
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"id": "2b279cae",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Output{value=false, type=boolean, score=0.0, name='PaidLoan'}'"
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"'Output{value=false, type=boolean, score=0.7835956811904907, name='PaidLoan'}'"
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]
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},
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"execution_count": 26,
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -788,7 +794,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 29,
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"id": "18fff350",
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"metadata": {},
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"outputs": [],
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},
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"execution_count": 30,
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"id": "3a4815d9",
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"metadata": {},
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{
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"execution_count": 31,
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"cell_type": "code",
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{
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"cell_type": "code",
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{
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{
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"cell_type": "code",
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"execution_count": 36,
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"outputs": [
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"data": {
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"text/plain": [
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"'Output{value=true, type=boolean, score=0.0, name='PaidLoan'}'"
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"'Output{value=true, type=boolean, score=0.6006738543510437, name='PaidLoan'}'"
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]
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},
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"execution_count": 35,
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"execution_count": 36,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -956,7 +962,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"id": "6e91cb5f",
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"metadata": {},
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},
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{
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"execution_count": 40,
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},
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{
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"execution_count": 41,
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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{
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"data": {
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"text/plain": [
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"'Output{value=true, type=boolean, score=0.0, name='PaidLoan'}'"
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"'Output{value=true, type=boolean, score=0.5038489103317261, name='PaidLoan'}'"
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]
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},
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"execution_count": 41,
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"execution_count": 42,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"id": "0557e545",
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"output_type": "stream",
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"text": [
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"Feature 'LoanDuration': 60.0 -> 56.947228037333545\n",
1210-
"Feature 'IncomeFromSocialWelfare': 53.0 -> 60.0\n",
1211-
"Feature 'FreeCash': 10.92 -> 10.914352713171315\n",
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"Feature 'Tenant': true -> false\n"
1216+
"Feature 'IncomeFromSocialWelfare': 53.0 -> 59.6876474017064\n",
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"Feature 'FreeCash': 10.92 -> 10.914352713171315\n"
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]
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}
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],

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