Run
8148666

Run 8148666

Task 119 (Learning Curve) zoo Uploaded 18-10-2017 by Toon Van Craenendonck
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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)
Issue #Downvotes for this reason By


Flow

TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator= sklearn.ensemble.forest.RandomForestClassifier)(1)Automatically created scikit-learn flow.
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_cv{"oml-python:serialized_object": "cv_object", "value": {"name": "sklearn.model_selection._split.StratifiedKFold", "parameters": {"n_splits": "2", "random_state": "62501", "shuffle": "true"}}}
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_error_score"raise"
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_fit_params{}
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_iidtrue
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_iter5
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_jobs1
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_param_distributions{"bootstrap": [true, false], "criterion": ["gini", "entropy"], "max_depth": [3, null], "max_features": [1, 2, 3, 4], "min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10]}
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_random_state12172
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_refittrue
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_return_train_scoretrue
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_scoringnull
TEST69fc2ef738sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose0
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_bootstraptrue
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_class_weightnull
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_criterion"gini"
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_max_depthnull
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_max_features"auto"
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_max_leaf_nodesnull
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_split1e-07
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_leaf1
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_split2
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_n_estimators5
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_n_jobs1
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_oob_scorefalse
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_random_state33003
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_verbose0
TEST69fc2ef738sklearn.ensemble.forest.RandomForestClassifier(1)_warm_startfalse

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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

0 Evaluation measures