Run
1849418

Run 1849418

Task 145677 (Supervised Classification) Bioresponse Uploaded 10-03-2017 by Ruud Andriessen
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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).
  • Fri_Mar_10_13.35.24_2017 NumPy_1.12.0. Python_3.5.1. run_task SciPy_0.18.1. sklearn.pipeline.Pipeline Sklearn_0.18.1.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(feature_select=sklearn.feature_selection.from_mod el.SelectFromModel(estimator=sklearn.ensemble.forest.RandomForestClassifier ),classifier=sklearn.svm.classes.SVC)(1)Automatically created scikit-learn flow.
sklearn.svm.classes.SVC(5)_C10
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)_gamma0.01
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_featuresauto
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_estimators256
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobs-1
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_scoreFalse
sklearn.ensemble.forest.RandomForestClassifier(16)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(16)_warm_startFalse
sklearn.pipeline.Pipeline(feature_select=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.RandomForestClassifier),classifier=sklearn.svm.classes.SVC)(1)_steps[('feature_select', SelectFromModel(estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy', 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=256, n_jobs=-1, oob_score=False, random_state=1, verbose=0, warm_start=False), prefit=False, threshold=None)), ('classifier', SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False))]
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_estimatorRandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy', 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=256, n_jobs=-1, oob_score=False, random_state=1, verbose=0, warm_start=False)
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_prefitFalse
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_thresholdNone

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