Run
10436956

Run 10436956

Task 3560 (Supervised Classification) analcatdata_dmft Uploaded 01-02-2020 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_jobsnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)_param_distributions{"l2_regularization": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "scipy.stats._continuous_distns.reciprocal_gen", "a": -Infinity, "b": Infinity, "args": [0.0001, 1], "kwds": {}}}, "learning_rate": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "scipy.stats._continuous_distns.reciprocal_gen", "a": -Infinity, "b": Infinity, "args": [0.0001, 1], "kwds": {}}}, "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_state7812
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.

18 Evaluation measures

Area under ROC curve
0.5655 ± 0.0561
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.2112 ± 0.0361
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0503 ± 0.0394
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0818 ± 0.0316
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2726 ± 0.0049
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2774 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.212 ± 0.0329
Cross-validation details (10-fold Crossvalidation)
Number of instances
797
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.2139 ± 0.0409
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.212 ± 0.0329
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.5803 ± 0.0024
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9828 ± 0.0175
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3724 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3754 ± 0.0097
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0079 ± 0.0258
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.21 ± 0.0332
Cross-validation details (10-fold Crossvalidation)