Run
5974367

Run 5974367

Task 31 (Supervised Classification) credit-g Uploaded 16-07-2017 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,variencethres hold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classif ier=sklearn.svm.classes.SVC)(1)Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(2)_axis0
openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features[0, 2, 3, 5, 6, 8, 9, 11, 13, 14, 16, 18, 19]
openmlstudy14.preprocessing.ConditionalImputer(2)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[0, 2, 3, 5, 6, 8, 9, 11, 13, 14, 16, 18, 19]
sklearn.preprocessing.data.OneHotEncoder(7)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(7)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(7)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(7)_sparsefalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold0.0
sklearn.svm.classes.SVC(8)_C352.0974754979511
sklearn.svm.classes.SVC(8)_cache_size200
sklearn.svm.classes.SVC(8)_class_weightnull
sklearn.svm.classes.SVC(8)_coef00.0
sklearn.svm.classes.SVC(8)_decision_function_shapenull
sklearn.svm.classes.SVC(8)_degree3
sklearn.svm.classes.SVC(8)_gamma0.00010087060051824772
sklearn.svm.classes.SVC(8)_kernel"rbf"
sklearn.svm.classes.SVC(8)_max_iter-1
sklearn.svm.classes.SVC(8)_probabilitytrue
sklearn.svm.classes.SVC(8)_random_state15605
sklearn.svm.classes.SVC(8)_shrinkingfalse
sklearn.svm.classes.SVC(8)_tol0.018360330584813355
sklearn.svm.classes.SVC(8)_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
0.6293 ± 0.0895
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5821 ± 0.0172
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0046 ± 0.034
Cross-validation details (10-fold Crossvalidation)
KB relative information score
14.7457 ± 2.9106
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4097 ± 0.008
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5996 ± 0.155
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.697 ± 0.0149
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8818
Cross-validation details (10-fold Crossvalidation)
Recall
0.697 ± 0.0149
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9751 ± 0.019
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4505 ± 0.0085
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9831 ± 0.0186
Cross-validation details (10-fold Crossvalidation)