Run
1849373

Run 1849373

Task 145677 (Supervised Classification) Bioresponse Uploaded 09-03-2017 by János Szedelényi
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Client side error: predict_proba is not available when probability=False
Evaluation Engine Exception: Required output files not present (e.g., arff predictions).
  • NumPy_1.12.0. Python_3.5.2. run_task SciPy_0.18.1. sklearn.ensemble.voting_classifier.VotingClassifier Sklearn_0.18.1. Thu_Mar__9_22.28.16_2017
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Flow

sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ense mble.forest.RandomForestClassifier,voting=sklearn.ensemble.forest.ExtraTree sClassifier,weights=sklearn.svm.classes.SVC)(1)Automatically created scikit-learn flow.
sklearn.svm.classes.SVC(5)_C512
sklearn.svm.classes.SVC(5)_cache_size200
sklearn.svm.classes.SVC(5)_class_weightNone
sklearn.svm.classes.SVC(5)_coef00.0
sklearn.svm.classes.SVC(5)_decision_function_shapeNone
sklearn.svm.classes.SVC(5)_degree3
sklearn.svm.classes.SVC(5)_gammaauto
sklearn.svm.classes.SVC(5)_kernelrbf
sklearn.svm.classes.SVC(5)_max_iter-1
sklearn.svm.classes.SVC(5)_probabilityFalse
sklearn.svm.classes.SVC(5)_random_stateNone
sklearn.svm.classes.SVC(5)_shrinkingTrue
sklearn.svm.classes.SVC(5)_tol0.001
sklearn.svm.classes.SVC(5)_verboseFalse
sklearn.ensemble.forest.RandomForestClassifier(16)_bootstrapTrue
sklearn.ensemble.forest.RandomForestClassifier(16)_class_weightNone
sklearn.ensemble.forest.RandomForestClassifier(16)_criterionentropy
sklearn.ensemble.forest.RandomForestClassifier(16)_max_depthNone
sklearn.ensemble.forest.RandomForestClassifier(16)_max_features0.1
sklearn.ensemble.forest.RandomForestClassifier(16)_max_leaf_nodesNone
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_n_estimators500
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobs-1
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_scoreFalse
sklearn.ensemble.forest.RandomForestClassifier(16)_random_state123
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(16)_warm_startFalse
sklearn.ensemble.forest.ExtraTreesClassifier(5)_bootstrapFalse
sklearn.ensemble.forest.ExtraTreesClassifier(5)_class_weightNone
sklearn.ensemble.forest.ExtraTreesClassifier(5)_criteriongini
sklearn.ensemble.forest.ExtraTreesClassifier(5)_max_depthNone
sklearn.ensemble.forest.ExtraTreesClassifier(5)_max_featuresauto
sklearn.ensemble.forest.ExtraTreesClassifier(5)_max_leaf_nodesNone
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_impurity_split1e-07
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_samples_leaf1
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_samples_split2
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.ExtraTreesClassifier(5)_n_estimators500
sklearn.ensemble.forest.ExtraTreesClassifier(5)_n_jobs-1
sklearn.ensemble.forest.ExtraTreesClassifier(5)_oob_scoreFalse
sklearn.ensemble.forest.ExtraTreesClassifier(5)_random_state444
sklearn.ensemble.forest.ExtraTreesClassifier(5)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(5)_warm_startFalse
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=sklearn.ensemble.forest.ExtraTreesClassifier,weights=sklearn.svm.classes.SVC)(1)_estimatorsRandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy', max_depth=None, max_features=0.1, max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1, oob_score=False, random_state=123, verbose=0, warm_start=False)
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=sklearn.ensemble.forest.ExtraTreesClassifier,weights=sklearn.svm.classes.SVC)(1)_n_jobs-1
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=sklearn.ensemble.forest.ExtraTreesClassifier,weights=sklearn.svm.classes.SVC)(1)_votingExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1, oob_score=False, random_state=444, verbose=0, warm_start=False)
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=sklearn.ensemble.forest.ExtraTreesClassifier,weights=sklearn.svm.classes.SVC)(1)_weightsSVC(C=512, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=None, degree=3, gamma='auto', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)

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