OpenML
8976777

Run 8976777

Task 3485 (Supervised Classification) scene Uploaded 08-04-2018 by Jan van Rijn
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  • openml-pimp openml-python Sklearn_0.18.1.
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Flow

sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona lImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklea rn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_sele ction.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.S VC)(2)Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(5)_axis0
openmlstudy14.preprocessing.ConditionalImputer(5)_categorical_features[294, 295, 296, 297, 298]
openmlstudy14.preprocessing.ConditionalImputer(5)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(5)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(5)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(5)_strategy"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(5)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(5)_verbose0
sklearn.preprocessing.data.OneHotEncoder(9)_categorical_features[294, 295, 296, 297, 298]
sklearn.preprocessing.data.OneHotEncoder(9)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(9)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(9)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(9)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(7)_threshold0.0
sklearn.svm.classes.SVC(11)_C85106.8473007141
sklearn.svm.classes.SVC(11)_cache_size200
sklearn.svm.classes.SVC(11)_class_weightnull
sklearn.svm.classes.SVC(11)_coef0-0.6570221892748853
sklearn.svm.classes.SVC(11)_decision_function_shapenull
sklearn.svm.classes.SVC(11)_degree4
sklearn.svm.classes.SVC(11)_gamma2.6608183750885306e-05
sklearn.svm.classes.SVC(11)_kernel"sigmoid"
sklearn.svm.classes.SVC(11)_max_iter-1
sklearn.svm.classes.SVC(11)_probabilitytrue
sklearn.svm.classes.SVC(11)_random_state63795
sklearn.svm.classes.SVC(11)_shrinkingtrue
sklearn.svm.classes.SVC(11)_tol5.6795033377827175e-05
sklearn.svm.classes.SVC(11)_verbosefalse
sklearn.preprocessing.data.StandardScaler(7)_copytrue
sklearn.preprocessing.data.StandardScaler(7)_with_meanfalse
sklearn.preprocessing.data.StandardScaler(7)_with_stdtrue

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
0.9734 ± 0.0222
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.965 ± 0.009
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8806 ± 0.0312
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1902.6634 ± 9.0537
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0583 ± 0.0085
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2942 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Number of instances
2407
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9649 ± 0.009
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9651 ± 0.009
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.6786
Cross-validation details (10-fold Crossvalidation)
Recall
0.9651 ± 0.009
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1983 ± 0.0293
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3834 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1725 ± 0.0209
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4499 ± 0.0553
Cross-validation details (10-fold Crossvalidation)