Run
1852381

Run 1852381

Task 145677 (Supervised Classification) Bioresponse Uploaded 13-03-2017 by Stanley Clark
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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).
  • Mon_Mar_13_10.04.09_2017 NumPy_1.12.0. Python_3.5.2. run_task SciPy_0.18.1. sklearn.pipeline.Pipeline Sklearn_0.18.1.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standar dScaler,selectfrommodel=sklearn.feature_selection.from_model.SelectFromMode l(estimator=sklearn.svm.classes.LinearSVC),svc=sklearn.svm.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.feature_selection.from_model.SelectFromModel(estimator=sklearn.svm.classes.LinearSVC)(1)_estimatorLinearSVC(C=0.01, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l1', random_state=None, tol=0.0001, verbose=0)
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.svm.classes.LinearSVC)(1)_prefitFalse
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.svm.classes.LinearSVC)(1)_thresholdNone
sklearn.svm.classes.LinearSVC(2)_C0.01
sklearn.svm.classes.LinearSVC(2)_class_weightNone
sklearn.svm.classes.LinearSVC(2)_dualFalse
sklearn.svm.classes.LinearSVC(2)_fit_interceptTrue
sklearn.svm.classes.LinearSVC(2)_intercept_scaling1
sklearn.svm.classes.LinearSVC(2)_losssquared_hinge
sklearn.svm.classes.LinearSVC(2)_max_iter1000
sklearn.svm.classes.LinearSVC(2)_multi_classovr
sklearn.svm.classes.LinearSVC(2)_penaltyl1
sklearn.svm.classes.LinearSVC(2)_random_stateNone
sklearn.svm.classes.LinearSVC(2)_tol0.0001
sklearn.svm.classes.LinearSVC(2)_verbose0
sklearn.preprocessing.data.StandardScaler(1)_copyTrue
sklearn.preprocessing.data.StandardScaler(1)_with_meanTrue
sklearn.preprocessing.data.StandardScaler(1)_with_stdTrue
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.svm.classes.LinearSVC),svc=sklearn.svm.classes.SVC)(1)_steps[('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('selectfrommodel', SelectFromModel(estimator=LinearSVC(C=0.01, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l1', random_state=None, tol=0.0001, verbose=0), prefit=False, threshold=None)), ('svc', 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