Run
8813823

Run 8813823

Task 14967 (Supervised Classification) cjs Uploaded 23-01-2018 by Jan van Rijn
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  • openml-pimp openml-python Sklearn_0.18.1. study_71
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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)(1)Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(6)_axis0
openmlstudy14.preprocessing.ConditionalImputer(6)_categorical_features[0, 1]
openmlstudy14.preprocessing.ConditionalImputer(6)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(6)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(6)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(6)_verbose0
sklearn.preprocessing.data.OneHotEncoder(18)_categorical_features[0, 1]
sklearn.preprocessing.data.OneHotEncoder(18)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(18)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(18)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(18)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(12)_threshold0.0
sklearn.preprocessing.data.StandardScaler(6)_copytrue
sklearn.preprocessing.data.StandardScaler(6)_with_meanfalse
sklearn.preprocessing.data.StandardScaler(6)_with_stdtrue
sklearn.svm.classes.SVC(17)_C52.38516752859237
sklearn.svm.classes.SVC(17)_cache_size200
sklearn.svm.classes.SVC(17)_class_weightnull
sklearn.svm.classes.SVC(17)_coef0-0.7837094470126089
sklearn.svm.classes.SVC(17)_decision_function_shapenull
sklearn.svm.classes.SVC(17)_degree1
sklearn.svm.classes.SVC(17)_gamma0.0031968448323518267
sklearn.svm.classes.SVC(17)_kernel"sigmoid"
sklearn.svm.classes.SVC(17)_max_iter-1
sklearn.svm.classes.SVC(17)_probabilitytrue
sklearn.svm.classes.SVC(17)_random_state11759
sklearn.svm.classes.SVC(17)_shrinkingfalse
sklearn.svm.classes.SVC(17)_tol0.04686879577908685
sklearn.svm.classes.SVC(17)_verbosefalse

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
0.956 ± 0.1479
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8562 ± 0.029
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8203 ± 0.2877
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1746.1913 ± 65.6744
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1364 ± 0.0572
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2729 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
2796
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8764 ± 0.0229
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8544 ± 0.216
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.5207
Cross-validation details (10-fold Crossvalidation)
Recall
0.8544 ± 0.216
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4999 ± 0.2097
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3694 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2319 ± 0.0585
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6279 ± 0.1583
Cross-validation details (10-fold Crossvalidation)