Run
10560182

Run 10560182

Task 9957 (Supervised Classification) qsar-biodeg Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemb le.forest.ExtraTreesClassifier)(2)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.forest.ExtraTreesClassifier)(2)_cvnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_error_score"raise"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_fit_params{}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_iidtrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_n_iter10
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_n_jobs1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_param_distributions{"bootstrap": [true, false], "criterion": ["gini", "entropy"], "max_features": [0.05, 0.1, 0.15000000000000002, 0.2, 0.25, 0.3, 0.35000000000000003, 0.4, 0.45, 0.5, 0.55, 0.6000000000000001, 0.6500000000000001, 0.7000000000000001, 0.7500000000000001, 0.8, 0.8500000000000001, 0.9000000000000001, 0.9500000000000001, 1.0], "min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_random_state18597
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_return_train_scoretrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(15)_bootstrapfalse
sklearn.ensemble.forest.ExtraTreesClassifier(15)_class_weightnull
sklearn.ensemble.forest.ExtraTreesClassifier(15)_criterion"gini"
sklearn.ensemble.forest.ExtraTreesClassifier(15)_max_depthnull
sklearn.ensemble.forest.ExtraTreesClassifier(15)_max_features"auto"
sklearn.ensemble.forest.ExtraTreesClassifier(15)_max_leaf_nodesnull
sklearn.ensemble.forest.ExtraTreesClassifier(15)_min_impurity_split1e-07
sklearn.ensemble.forest.ExtraTreesClassifier(15)_min_samples_leaf1
sklearn.ensemble.forest.ExtraTreesClassifier(15)_min_samples_split2
sklearn.ensemble.forest.ExtraTreesClassifier(15)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.ExtraTreesClassifier(15)_n_estimators100
sklearn.ensemble.forest.ExtraTreesClassifier(15)_n_jobs-1
sklearn.ensemble.forest.ExtraTreesClassifier(15)_oob_scorefalse
sklearn.ensemble.forest.ExtraTreesClassifier(15)_random_state44044
sklearn.ensemble.forest.ExtraTreesClassifier(15)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(15)_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

0.9302 ± 0.0322
Per class
Cross-validation details (10-fold Crossvalidation)
0.8635 ± 0.0256
Per class
Cross-validation details (10-fold Crossvalidation)
0.6913 ± 0.0586
Cross-validation details (10-fold Crossvalidation)
0.5635 ± 0.043
Cross-validation details (10-fold Crossvalidation)
0.2049 ± 0.0172
Cross-validation details (10-fold Crossvalidation)
0.4472 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
0.8654 ± 0.0246
Cross-validation details (10-fold Crossvalidation)
1055
Per class
Cross-validation details (10-fold Crossvalidation)
0.8639 ± 0.0257
Per class
Cross-validation details (10-fold Crossvalidation)
0.8654 ± 0.0246
Cross-validation details (10-fold Crossvalidation)
0.9223 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
0.4581 ± 0.0386
Cross-validation details (10-fold Crossvalidation)
0.4728 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
0.3072 ± 0.0291
Cross-validation details (10-fold Crossvalidation)
0.6497 ± 0.0613
Cross-validation details (10-fold Crossvalidation)
0.8371 ± 0.0316
Cross-validation details (10-fold Crossvalidation)