Run
6122841

Run 6122841

Task 3948 (Supervised Classification) KDDCup09_upselling Uploaded 18-08-2017 by Jan van Rijn
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Flow

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeli ne.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hoten coding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.f eature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.en semble.forest.RandomForestClassifier))(2)Automatically created scikit-learn flow.
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_cvnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_error_score"raise"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_fit_params{}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_iidtrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_n_iter50
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_n_jobs1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_param_distributions{"classifier__min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], "classifier__bootstrap": [true, false], "classifier__criterion": ["gini", "entropy"], "imputation__strategy": ["mean", "median", "most_frequent"]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_random_state1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_return_train_scoretrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(2)_verbose0
openmlstudy14.preprocessing.ConditionalImputer(4)_axis0
openmlstudy14.preprocessing.ConditionalImputer(4)_categorical_features[190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228]
openmlstudy14.preprocessing.ConditionalImputer(4)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(4)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(4)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(4)_strategy"median"
openmlstudy14.preprocessing.ConditionalImputer(4)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(4)_verbose0
sklearn.preprocessing.data.OneHotEncoder(8)_categorical_features[190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228]
sklearn.preprocessing.data.OneHotEncoder(8)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(8)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(8)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(8)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(6)_threshold0.0
sklearn.ensemble.forest.RandomForestClassifier(23)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(23)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(23)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(23)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(23)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(23)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(23)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(23)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(23)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(23)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(23)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(23)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(23)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(23)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(23)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(23)_warm_startfalse

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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

17 Evaluation measures

Area under ROC curve
0.842 ± 0.0118
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9439 ± 0.0019
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.5419 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
KB relative information score
3586.0876 ± 175.809
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0872 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1364 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Number of instances
50000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9459 ± 0.0022
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9509 ± 0.0016
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3794
Cross-validation details (10-fold Crossvalidation)
Recall
0.9509 ± 0.0016
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6394 ± 0.0132
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2612 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2112 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8084 ± 0.0116
Cross-validation details (10-fold Crossvalidation)