Run
10560103

Run 10560103

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.linear _model.logistic.LogisticRegression)(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.linear_model.logistic.LogisticRegression)(2)_cvnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_error_score"raise"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_fit_params{}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_iidtrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_n_iter10
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_n_jobs1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_param_distributions{"C": [0.0001, 0.001, 0.01, 0.1, 1.0, 10, 100, 1000, 10000, 1000000], "solver": ["liblinear", "sag"], "tol": [1e-06, 1e-05, 0.0001, 0.001]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_random_state46395
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_return_train_scoretrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression)(2)_verbose0
sklearn.linear_model.logistic.LogisticRegression(36)_C1.0
sklearn.linear_model.logistic.LogisticRegression(36)_class_weightnull
sklearn.linear_model.logistic.LogisticRegression(36)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(36)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(36)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(36)_max_iter100
sklearn.linear_model.logistic.LogisticRegression(36)_multi_class"ovr"
sklearn.linear_model.logistic.LogisticRegression(36)_n_jobs1
sklearn.linear_model.logistic.LogisticRegression(36)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(36)_random_state7505
sklearn.linear_model.logistic.LogisticRegression(36)_solver"liblinear"
sklearn.linear_model.logistic.LogisticRegression(36)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(36)_verbose0
sklearn.linear_model.logistic.LogisticRegression(36)_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
0.9274 ± 0.0117
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7418 ± 0.0384
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3229 ± 0.2657
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2577 ± 0.1035
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3798 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8608 ± 0.021
Cross-validation details (10-fold Crossvalidation)
Number of instances
625
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8608 ± 0.021
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.3181 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6785 ± 0.2716
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
0.3388 ± 0.089
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7779 ± 0.2033
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6227 ± 0.0141
Cross-validation details (10-fold Crossvalidation)