Run
6162266

Run 6162266

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"median"
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)_C0.11984937337191003
sklearn.svm.classes.SVC(8)_cache_size200
sklearn.svm.classes.SVC(8)_class_weightnull
sklearn.svm.classes.SVC(8)_coef00.35600211914774116
sklearn.svm.classes.SVC(8)_decision_function_shapenull
sklearn.svm.classes.SVC(8)_degree2
sklearn.svm.classes.SVC(8)_gamma0.05982519796553969
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_state1116
sklearn.svm.classes.SVC(8)_shrinkingtrue
sklearn.svm.classes.SVC(8)_tol0.0001071385881785686
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
0.7111 ± 0.0071
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6539 ± 0.0065
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3069 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
KB relative information score
14896.9645 ± 43.2238
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4333 ± 0.0018
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
0.6589 ± 0.007
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6578 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9976
Cross-validation details (10-fold Crossvalidation)
Recall
0.6578 ± 0.0066
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8694 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4992 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4644 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9303 ± 0.0047
Cross-validation details (10-fold Crossvalidation)