Run
9954267

Run 9954267

Task 115 (Learning Curve) vowel Uploaded 09-01-2019 by Continuous Integration
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Evaluation Engine Exception: Illegal combination of evaluation measure attributes (repeat, fold, sample): Measure(s): mean_absolute_error(1, 1, 0), predictive_accuracy(1, 1, 0), root_mean_squared_error(1, 1, 0), prior_entropy(1, 1, 0), total_cost(1, 1, 0), f_measure(1, 1, 0), root_relative_squared_error(1, 1, 0), area_under_roc_curve(1, 1, 0), mean_prior_absolute_error(1, 1, 0), precision(1, 1, 0), average_cost(1, 1, 0), number_of_instances(1, 1, 0), recall(1, 1, 0), kb_relative_information_score(1, 1, 0), kappa(1, 1, 0), root_mean_prior_squared_error(1, 1, 0), relative_absolute_error(1, 1, 0), mean_absolute_error(1, 1, 1), predictive_accuracy(1, 1, 1), root_mean_squared_error(1, 1, 1), prior_entropy(1, 1, 1), total_cost(1, 1, 1), f_measure(1, 1, 1), root_relative_squared_error(1, 1, 1), area_under_roc_curve(1, 1, 1), mean_prior_absolute_error(1, 1, 1), precision(1, 1, 1), average_cost(1, 1, 1), number_of_instances(1, 1, 1), recall(1, 1, 1), kb_relative_information_score(1, 1, 1), kappa(1, 1, ... (message cut-off due to excessive length)
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Flow

sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler, boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator =sklearn.tree.tree.DecisionTreeClassifier))(1)Automatically created scikit-learn flow.
sklearn.preprocessing.data.StandardScaler(14)_copytrue
sklearn.preprocessing.data.StandardScaler(14)_with_meanfalse
sklearn.preprocessing.data.StandardScaler(14)_with_stdtrue
sklearn.tree.tree.DecisionTreeClassifier(29)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(29)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(29)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(29)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(29)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(29)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(29)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(29)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(29)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(29)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(29)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(29)_random_state61212
sklearn.tree.tree.DecisionTreeClassifier(29)_splitter"best"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_algorithm"SAMME.R"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_learning_rate1.0
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_n_estimators50
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_random_state10943
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memorynull

Result files

xml
Description

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

arff
Predictions

ARFF file with instance-level predictions generated by the model.

0 Evaluation measures