Run
8816567

Run 8816567

Task 14964 (Supervised Classification) artificial-characters Uploaded 24-01-2018 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[]
openmlstudy14.preprocessing.ConditionalImputer(6)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(6)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(6)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy"mean"
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)_C73.27419659274894
sklearn.svm.classes.SVC(17)_cache_size200
sklearn.svm.classes.SVC(17)_class_weightnull
sklearn.svm.classes.SVC(17)_coef00.7312130604547786
sklearn.svm.classes.SVC(17)_decision_function_shapenull
sklearn.svm.classes.SVC(17)_degree1
sklearn.svm.classes.SVC(17)_gamma0.0004314996299103853
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_state19820
sklearn.svm.classes.SVC(17)_shrinkingtrue
sklearn.svm.classes.SVC(17)_tol3.573241555258134e-05
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
0.8511 ± 0.0066
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.3765 ± 0.0201
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3167 ± 0.0208
Cross-validation details (10-fold Crossvalidation)
KB relative information score
3663.6036 ± 6.5956
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1456 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.179 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
10218
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.3948 ± 0.0249
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.3884 ± 0.0187
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.2849
Cross-validation details (10-fold Crossvalidation)
Recall
0.3884 ± 0.0187
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8135 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2992 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2668 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8918 ± 0.0049
Cross-validation details (10-fold Crossvalidation)