Run
10560367

Run 10560367

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_state37270
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_state57162
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

Area under ROC curve
0.9334 ± 0.0283
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8644 ± 0.0291
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6933 ± 0.0656
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5534 ± 0.0385
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2094 ± 0.0156
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4472 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8664 ± 0.0285
Cross-validation details (10-fold Crossvalidation)
Number of instances
1055
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8649 ± 0.0298
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8664 ± 0.0285
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9223 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4682 ± 0.0347
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4728 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3076 ± 0.0263
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6505 ± 0.0554
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8378 ± 0.0337
Cross-validation details (10-fold Crossvalidation)