Run
8804155

Run 8804155

Task 9967 (Supervised Classification) steel-plates-fault Uploaded 22-01-2018 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"most_frequent"
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)_C18.177553311981796
sklearn.svm.classes.SVC(17)_cache_size200
sklearn.svm.classes.SVC(17)_class_weightnull
sklearn.svm.classes.SVC(17)_coef0-0.172530630742034
sklearn.svm.classes.SVC(17)_decision_function_shapenull
sklearn.svm.classes.SVC(17)_degree4
sklearn.svm.classes.SVC(17)_gamma0.0002023846020279116
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_state30069
sklearn.svm.classes.SVC(17)_shrinkingfalse
sklearn.svm.classes.SVC(17)_tol0.005824107357384776
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
0.6157 ± 0.0508
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5292 ± 0.0295
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0295 ± 0.0869
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-6.9836 ± 5.7221
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4461 ± 0.0092
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4531 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Number of instances
1941
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5175 ± 0.0927
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6198 ± 0.0549
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9313
Cross-validation details (10-fold Crossvalidation)
Recall
0.6198 ± 0.0549
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9846 ± 0.0205
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4759 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4703 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9882 ± 0.0164
Cross-validation details (10-fold Crossvalidation)