Run
1852626

Run 1852626

Task 145677 (Supervised Classification) Bioresponse Uploaded 15-03-2017 by Angelo Majoor
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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_3.6.0. run_task SciPy_0.19.0. sklearn.pipeline.Pipeline Sklearn_0.18.1. Wed_Mar_15_18.10.59_2017
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer, OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=skle arn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.s vm.classes.SVC)(1)Automatically created scikit-learn flow.
sklearn.svm.classes.SVC(5)_C1.0
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.preprocessing.imputation.Imputer(3)_axis0
sklearn.preprocessing.imputation.Imputer(3)_copyTrue
sklearn.preprocessing.imputation.Imputer(3)_missing_valuesNaN
sklearn.preprocessing.imputation.Imputer(3)_strategymedian
sklearn.preprocessing.imputation.Imputer(3)_verbose0
sklearn.preprocessing.data.OneHotEncoder(3)_categorical_featuresall
sklearn.preprocessing.data.OneHotEncoder(3)_dtype
sklearn.preprocessing.data.OneHotEncoder(3)_handle_unknownignore
sklearn.preprocessing.data.OneHotEncoder(3)_n_valuesauto
sklearn.preprocessing.data.OneHotEncoder(3)_sparseFalse
sklearn.feature_selection.univariate_selection.SelectKBest(1)_k100
sklearn.feature_selection.univariate_selection.SelectKBest(1)_score_func
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.svm.classes.SVC)(1)_steps[('Imputer', Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)), ('OneHotEncoder', OneHotEncoder(categorical_features='all', dtype=, handle_unknown='ignore', n_values='auto', sparse=False)), ('Feature_Removal', SelectKBest(k=100, score_func=)), ('Classifier', SVC(C=1.0, 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

xml
Description

XML file describing the run, including user-defined evaluation measures.

0 Evaluation measures