Run
8980666

Run 8980666

Task 23 (Supervised Classification) cmc Uploaded 08-04-2018 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[1, 2, 4, 5, 6, 7, 8]
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[1, 2, 4, 5, 6, 7, 8]
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)_C4436.170797903853
sklearn.svm.classes.SVC(11)_cache_size200
sklearn.svm.classes.SVC(11)_class_weightnull
sklearn.svm.classes.SVC(11)_coef00.8395102034756814
sklearn.svm.classes.SVC(11)_decision_function_shapenull
sklearn.svm.classes.SVC(11)_degree5
sklearn.svm.classes.SVC(11)_gamma1.3078717746883638e-05
sklearn.svm.classes.SVC(11)_kernel"poly"
sklearn.svm.classes.SVC(11)_max_iter-1
sklearn.svm.classes.SVC(11)_probabilitytrue
sklearn.svm.classes.SVC(11)_random_state22336
sklearn.svm.classes.SVC(11)_shrinkingtrue
sklearn.svm.classes.SVC(11)_tol1.6984083006155194e-05
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
0.6831 ± 0.0432
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4905 ± 0.0479
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2092 ± 0.0726
Cross-validation details (10-fold Crossvalidation)
KB relative information score
215.1046 ± 3.5304
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.393 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
1473
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4899 ± 0.0533
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.501 ± 0.0476
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.5392
Cross-validation details (10-fold Crossvalidation)
Recall
0.501 ± 0.0476
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9122 ± 0.0205
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4641 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.442 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9523 ± 0.0213
Cross-validation details (10-fold Crossvalidation)