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157

Run 157

Task 119 (Supervised Classification) diabetes Uploaded 29-10-2019 by Continuous Integration
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TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklear n.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC ))(1)Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.
TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_cv"warn"
TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_error_score"raise-deprecating"
TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_iid"warn"
TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_n_jobsnull
TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_param_grid{"base_estimator__C": [0.01, 0.1, 10], "base_estimator__gamma": [0.01, 0.1, 10]}
TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_pre_dispatch"2*n_jobs"
TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_refittrue
>> clf = GridSearchCV(svc, parameters, cv=5) >>> clf.fit(iris.data, iris.target) ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS GridSearchCV(cv=5, error_score=..., estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., decision_function_shape='ovr', degree=..., gamma=..., kernel='...">TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_return_train_scorefalse
TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_scoringnull
TEST096561312bsklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC))(1)_verbose0
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_bootstraptrue
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_bootstrap_featuresfalse
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_max_features1.0
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_max_samples1.0
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_n_estimators10
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_n_jobsnull
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_oob_scorefalse
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_random_state33003
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_verbose0
TEST096561312bsklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)(1)_warm_startfalse
TEST096561312bsklearn.svm.classes.SVC(1)_C1.0
TEST096561312bsklearn.svm.classes.SVC(1)_cache_size200
TEST096561312bsklearn.svm.classes.SVC(1)_class_weightnull
TEST096561312bsklearn.svm.classes.SVC(1)_coef00.0
TEST096561312bsklearn.svm.classes.SVC(1)_decision_function_shape"ovr"
TEST096561312bsklearn.svm.classes.SVC(1)_degree3
TEST096561312bsklearn.svm.classes.SVC(1)_gamma"auto_deprecated"
TEST096561312bsklearn.svm.classes.SVC(1)_kernel"rbf"
TEST096561312bsklearn.svm.classes.SVC(1)_max_iter-1
TEST096561312bsklearn.svm.classes.SVC(1)_probabilityfalse
TEST096561312bsklearn.svm.classes.SVC(1)_random_state62501
TEST096561312bsklearn.svm.classes.SVC(1)_shrinkingtrue
TEST096561312bsklearn.svm.classes.SVC(1)_tol0.001
TEST096561312bsklearn.svm.classes.SVC(1)_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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

18 Evaluation measures

0.5627
Per class
Cross-validation details (10% Holdout set)
0.5737
Per class
Cross-validation details (10% Holdout set)
0.0944
Cross-validation details (10% Holdout set)
0.2054
Cross-validation details (10% Holdout set)
0.3518
Cross-validation details (10% Holdout set)
0.4589
Cross-validation details (10% Holdout set)
0.6482
Cross-validation details (10% Holdout set)
253
Per class
Cross-validation details (10% Holdout set)
0.6233
Per class
Cross-validation details (10% Holdout set)
0.6482
Cross-validation details (10% Holdout set)
0.9463
Cross-validation details (10% Holdout set)
0.7665
Cross-validation details (10% Holdout set)
0.4813
Cross-validation details (10% Holdout set)
0.5705
Cross-validation details (10% Holdout set)
1.1854
Cross-validation details (10% Holdout set)
0.5396
Cross-validation details (10% Holdout set)