Run
6216753

Run 6216753

Task 146606 (Supervised Classification) higgs Uploaded 27-08-2017 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[]
openmlstudy14.preprocessing.ConditionalImputer(2)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy"mean"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[]
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)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold0.0
sklearn.svm.classes.SVC(8)_C69.28832967629725
sklearn.svm.classes.SVC(8)_cache_size200
sklearn.svm.classes.SVC(8)_class_weightnull
sklearn.svm.classes.SVC(8)_coef0-0.8437785697014795
sklearn.svm.classes.SVC(8)_decision_function_shapenull
sklearn.svm.classes.SVC(8)_degree4
sklearn.svm.classes.SVC(8)_gamma0.00022122620464489374
sklearn.svm.classes.SVC(8)_kernel"poly"
sklearn.svm.classes.SVC(8)_max_iter-1
sklearn.svm.classes.SVC(8)_probabilitytrue
sklearn.svm.classes.SVC(8)_random_state52085
sklearn.svm.classes.SVC(8)_shrinkingtrue
sklearn.svm.classes.SVC(8)_tol0.004372808899502816
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
0.4967 ± 0.0167
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.451 ± 0.0199
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0116 ± 0.0109
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-157.2664 ± 15.1356
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4989 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4984 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
98050
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4919 ± 0.0069
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5136 ± 0.0115
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9976
Cross-validation details (10-fold Crossvalidation)
Recall
0.5136 ± 0.0115
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0011 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4992 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4997 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0011 ± 0.001
Cross-validation details (10-fold Crossvalidation)