Run
10228363

Run 10228363

Task 167141 (Supervised Classification) churn Uploaded 04-06-2019 by Andreas Mueller
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  • openml-python Sklearn_0.22.dev0.
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Flow

sklearn.pipeline.Pipeline(polynomialfeatures=sklearn.preprocessing.data.Pol ynomialFeatures,logisticregression=sklearn.linear_model.logistic.LogisticRe gression)(1)Automatically created scikit-learn flow.
sklearn.linear_model.logistic.LogisticRegression(25)_C500
sklearn.linear_model.logistic.LogisticRegression(25)_class_weightnull
sklearn.linear_model.logistic.LogisticRegression(25)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(25)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(25)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(25)_l1_rationull
sklearn.linear_model.logistic.LogisticRegression(25)_max_iter100
sklearn.linear_model.logistic.LogisticRegression(25)_multi_class"auto"
sklearn.linear_model.logistic.LogisticRegression(25)_n_jobsnull
sklearn.linear_model.logistic.LogisticRegression(25)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(25)_random_state36371
sklearn.linear_model.logistic.LogisticRegression(25)_solver"lbfgs"
sklearn.linear_model.logistic.LogisticRegression(25)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(25)_verbose0
sklearn.linear_model.logistic.LogisticRegression(25)_warm_startfalse
sklearn.pipeline.Pipeline(polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.Pipeline(polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "polynomialfeatures", "step_name": "polynomialfeatures"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "logisticregression", "step_name": "logisticregression"}}]
sklearn.pipeline.Pipeline(polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(1)_verbosefalse
sklearn.preprocessing.data.PolynomialFeatures(4)_degree2
sklearn.preprocessing.data.PolynomialFeatures(4)_include_biastrue
sklearn.preprocessing.data.PolynomialFeatures(4)_interaction_onlytrue
sklearn.preprocessing.data.PolynomialFeatures(4)_order"C"

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

17 Evaluation measures

Area under ROC curve
0.6969 ± 0.0449
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8215 ± 0.012
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1516 ± 0.0615
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.119 ± 0.0436
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2248 ± 0.0062
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2429 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Number of instances
5000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8317 ± 0.0249
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8636 ± 0.0071
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5879 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
Recall
0.8636 ± 0.0071
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9253 ± 0.0245
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3484 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3338 ± 0.008
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9579 ± 0.0221
Cross-validation details (10-fold Crossvalidation)