Run
10560113

Run 10560113

Task 11 (Supervised Classification) balance-scale 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_state54452
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_state41068
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.

16 Evaluation measures

Area under ROC curve
0.952 ± 0.0105
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.822 ± 0.0568
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3715 ± 0.0371
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2471 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3798 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.904 ± 0.0308
Cross-validation details (10-fold Crossvalidation)
Number of instances
625
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.904 ± 0.0308
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.3181 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6507 ± 0.0415
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4356 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3107 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7133 ± 0.0273
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6539 ± 0.0213
Cross-validation details (10-fold Crossvalidation)