Run
9011395

Run 9011395

Task 146817 (Supervised Classification) steel-plates-fault Uploaded 10-04-2018 by Hilde Weerts
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  • openml-python Sklearn_0.19.1. study_98
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.Condition alImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=skle arn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_sel ection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1)Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features[]
sklearn.preprocessing.data.OneHotEncoder(17)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(17)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(17)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(17)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(11)_threshold0.0
sklearn.preprocessing.data.StandardScaler(5)_copytrue
sklearn.preprocessing.data.StandardScaler(5)_with_meanfalse
sklearn.preprocessing.data.StandardScaler(5)_with_stdtrue
sklearn.svm.classes.SVC(16)_C8217.442670270288
sklearn.svm.classes.SVC(16)_cache_size200
sklearn.svm.classes.SVC(16)_class_weightnull
sklearn.svm.classes.SVC(16)_coef00.9002236885455375
sklearn.svm.classes.SVC(16)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(16)_degree3
sklearn.svm.classes.SVC(16)_gamma2.3038174321962295
sklearn.svm.classes.SVC(16)_kernel"rbf"
sklearn.svm.classes.SVC(16)_max_iter-1
sklearn.svm.classes.SVC(16)_probabilityfalse
sklearn.svm.classes.SVC(16)_random_state1
sklearn.svm.classes.SVC(16)_shrinkingtrue
sklearn.svm.classes.SVC(16)_tol0.03989282154002149
sklearn.svm.classes.SVC(16)_verbosefalse
hyperimp.utils.preprocessing.ConditionalImputer(1)_axis0
hyperimp.utils.preprocessing.ConditionalImputer(1)_categorical_features[]
hyperimp.utils.preprocessing.ConditionalImputer(1)_copytrue
hyperimp.utils.preprocessing.ConditionalImputer(1)_fill_empty0
hyperimp.utils.preprocessing.ConditionalImputer(1)_missing_values"NaN"
hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy"mean"
hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy_nominal"most_frequent"
hyperimp.utils.preprocessing.ConditionalImputer(1)_verbose0
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1)_memorynull

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

0.649 ± 0.0183
Per class
Cross-validation details (10-fold Crossvalidation)
0.4564 ± 0.0279
Per class
Cross-validation details (10-fold Crossvalidation)
0.3309 ± 0.0395
Cross-validation details (10-fold Crossvalidation)
749.1073 ± 6.5673
Cross-validation details (10-fold Crossvalidation)
0.1313 ± 0.0069
Cross-validation details (10-fold Crossvalidation)
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
1941
Per class
Cross-validation details (10-fold Crossvalidation)
0.7699 ± 0.0065
Per class
Cross-validation details (10-fold Crossvalidation)
0.5404 ± 0.0242
Cross-validation details (10-fold Crossvalidation)
2.4138
Cross-validation details (10-fold Crossvalidation)
0.5404 ± 0.0242
Per class
Cross-validation details (10-fold Crossvalidation)
0.5905 ± 0.0309
Cross-validation details (10-fold Crossvalidation)
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.3624 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
1.087 ± 0.0283
Cross-validation details (10-fold Crossvalidation)