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10417571

Run 10417571

Task 3022 (Supervised Classification) vowel Uploaded 17-11-2019 by Nicolas Hug
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  • openml-python Sklearn_0.23.dev0.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemb le._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier )(3)Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_cvnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_error_scoreNaN
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_iid"deprecated"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_n_iter100
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_n_jobs-1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_param_distributions{"l2_regularization": [0, 0.01, 0.1], "learning_rate": [0.01, 0.1, 1], "max_depth": [5, 6, 7, 8, 9, 1000], "max_leaf_nodes": [30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "min_samples_leaf": [2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_random_state0
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_return_train_scorefalse
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_l2_regularization0.0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_learning_rate0.1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_max_bins255
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_max_depthnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_max_iter100
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_max_leaf_nodes31
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_min_samples_leaf20
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_n_iter_no_changenull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_random_state41647
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_scoringnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_tol1e-07
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_validation_fraction0.1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)_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.

17 Evaluation measures

Area under ROC curve
0.9982 ± 0.0012
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9505 ± 0.0202
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9456 ± 0.0219
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9505 ± 0.017
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0115 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1653
Cross-validation details (10-fold Crossvalidation)
Number of instances
990
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9508 ± 0.0172
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9505 ± 0.0199
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.4594
Cross-validation details (10-fold Crossvalidation)
Recall
0.9505 ± 0.0199
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0698 ± 0.0213
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2875
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0835 ± 0.0128
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.2904 ± 0.0445
Cross-validation details (10-fold Crossvalidation)