Run
1850690

Run 1850690

Task 145677 (Supervised Classification) Bioresponse Uploaded 10-03-2017 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).
  • Fri_Mar_10_22.15.11_2017 NumPy_1.12.0. Python_3.5.2. run_task SciPy_0.18.1. sklearn.ensemble.voting_classifier.VotingClassifier Sklearn_0.18.1.
Issue #Downvotes for this reason By


Flow

sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ense mble.forest.RandomForestClassifier,voting=xgboost.sklearn.XGBClassifier,wei ghts=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)_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_estimators10
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
xgboost.sklearn.XGBClassifier(3)_base_score0.5
xgboost.sklearn.XGBClassifier(3)_colsample_bylevel1
xgboost.sklearn.XGBClassifier(3)_colsample_bytree1
xgboost.sklearn.XGBClassifier(3)_gamma0
xgboost.sklearn.XGBClassifier(3)_learning_rate0.1
xgboost.sklearn.XGBClassifier(3)_max_delta_step0
xgboost.sklearn.XGBClassifier(3)_max_depth3
xgboost.sklearn.XGBClassifier(3)_min_child_weight1
xgboost.sklearn.XGBClassifier(3)_missingNone
xgboost.sklearn.XGBClassifier(3)_n_estimators10
xgboost.sklearn.XGBClassifier(3)_nthread-1
xgboost.sklearn.XGBClassifier(3)_objectivebinary:logistic
xgboost.sklearn.XGBClassifier(3)_reg_alpha0
xgboost.sklearn.XGBClassifier(3)_reg_lambda1
xgboost.sklearn.XGBClassifier(3)_scale_pos_weight1
xgboost.sklearn.XGBClassifier(3)_seed0
xgboost.sklearn.XGBClassifier(3)_silentTrue
xgboost.sklearn.XGBClassifier(3)_subsample1
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=xgboost.sklearn.XGBClassifier,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=10, 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=xgboost.sklearn.XGBClassifier,weights=sklearn.svm.classes.SVC)(1)_n_jobs-1
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=xgboost.sklearn.XGBClassifier,weights=sklearn.svm.classes.SVC)(1)_votingXGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3, min_child_weight=1, missing=None, n_estimators=10, nthread=-1, objective='binary:logistic', reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=0, silent=True, subsample=1)
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=xgboost.sklearn.XGBClassifier,weights=sklearn.svm.classes.SVC)(1)_weightsSVC(C=10, 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)

Result files

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