Run
8728848

Run 8728848

Task 9967 (Supervised Classification) steel-plates-fault Uploaded 29-12-2017 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"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)_C0.9356595186833928
sklearn.svm.classes.SVC(17)_cache_size200
sklearn.svm.classes.SVC(17)_class_weightnull
sklearn.svm.classes.SVC(17)_coef00.5073253581116093
sklearn.svm.classes.SVC(17)_decision_function_shapenull
sklearn.svm.classes.SVC(17)_degree1
sklearn.svm.classes.SVC(17)_gamma0.011459998854762489
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_state63572
sklearn.svm.classes.SVC(17)_shrinkingfalse
sklearn.svm.classes.SVC(17)_tol9.590673415120154e-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
0.7079 ± 0.0255
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6882 ± 0.0211
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3065 ± 0.0469
Cross-validation details (10-fold Crossvalidation)
KB relative information score
409.7325 ± 6.4655
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3508 ± 0.014
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
0.6862 ± 0.0209
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6909 ± 0.0221
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9313
Cross-validation details (10-fold Crossvalidation)
Recall
0.6909 ± 0.0221
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7743 ± 0.0309
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
0.4766 ± 0.0141
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0013 ± 0.0304
Cross-validation details (10-fold Crossvalidation)