Run
8851853

Run 8851853

Task 3896 (Supervised Classification) ada_agnostic Uploaded 27-01-2018 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)(1)Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(6)_axis0
openmlstudy14.preprocessing.ConditionalImputer(6)_categorical_features[]
openmlstudy14.preprocessing.ConditionalImputer(6)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(6)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(6)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy"median"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(6)_verbose0
sklearn.preprocessing.data.OneHotEncoder(18)_categorical_features[]
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)_C80.36873605937973
sklearn.svm.classes.SVC(17)_cache_size200
sklearn.svm.classes.SVC(17)_class_weightnull
sklearn.svm.classes.SVC(17)_coef0-0.908926328907014
sklearn.svm.classes.SVC(17)_decision_function_shapenull
sklearn.svm.classes.SVC(17)_degree3
sklearn.svm.classes.SVC(17)_gamma0.0429866790153919
sklearn.svm.classes.SVC(17)_kernel"poly"
sklearn.svm.classes.SVC(17)_max_iter-1
sklearn.svm.classes.SVC(17)_probabilitytrue
sklearn.svm.classes.SVC(17)_random_state53149
sklearn.svm.classes.SVC(17)_shrinkingtrue
sklearn.svm.classes.SVC(17)_tol0.00028782937137235493
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
0.7743 ± 0.0265
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7632 ± 0.0211
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3216 ± 0.0599
Cross-validation details (10-fold Crossvalidation)
KB relative information score
926.2107 ± 12.35
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2866 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3732 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
4562
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7812 ± 0.0298
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7955 ± 0.0183
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8085
Cross-validation details (10-fold Crossvalidation)
Recall
0.7955 ± 0.0183
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7679 ± 0.019
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4319 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.385 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8913 ± 0.0255
Cross-validation details (10-fold Crossvalidation)