Run
9202508

Run 9202508

Task 146817 (Supervised Classification) steel-plates-fault Uploaded 30-05-2018 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[]
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[]
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)_C15166.498096109495
sklearn.svm.classes.SVC(11)_cache_size200
sklearn.svm.classes.SVC(11)_class_weightnull
sklearn.svm.classes.SVC(11)_coef00.6657712802767766
sklearn.svm.classes.SVC(11)_decision_function_shapenull
sklearn.svm.classes.SVC(11)_degree2
sklearn.svm.classes.SVC(11)_gamma0.04496747764690361
sklearn.svm.classes.SVC(11)_kernel"rbf"
sklearn.svm.classes.SVC(11)_max_iter-1
sklearn.svm.classes.SVC(11)_probabilitytrue
sklearn.svm.classes.SVC(11)_random_state27743
sklearn.svm.classes.SVC(11)_shrinkingfalse
sklearn.svm.classes.SVC(11)_tol0.0013784554336868776
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
0.9126 ± 0.0118
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7187 ± 0.0257
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6361 ± 0.0332
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1229.3944 ± 4.1464
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1097 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Number of instances
1941
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7267 ± 0.0268
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7228 ± 0.0263
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.4138
Cross-validation details (10-fold Crossvalidation)
Recall
0.7228 ± 0.0263
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4934 ± 0.0189
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2292 ± 0.0084
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6875 ± 0.0248
Cross-validation details (10-fold Crossvalidation)