Run
9410

Run 9410

Task 119 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
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TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklear n.ensemble._forest.RandomForestClassifier)(1)Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "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 grid-search over a parameter grid.
TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_cv{"oml-python:serialized_object": "cv_object", "value": {"name": "sklearn.model_selection._split.StratifiedKFold", "parameters": {"n_splits": "2", "random_state": "62501", "shuffle": "true"}}}
TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_error_scoreNaN
TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_n_jobsnull
TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_param_grid[{"max_features": [2, 4]}, {"min_samples_leaf": [1, 10]}]
TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_refittrue
TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_return_train_scorefalse
TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_scoringnull
TESTf52a171b20sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose0
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_bootstraptrue
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_ccp_alpha0.0
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_class_weightnull
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_criterion"gini"
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_max_depthnull
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_max_features"auto"
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_max_leaf_nodesnull
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_max_samplesnull
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_decrease0.0
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_splitnull
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_leaf1
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_split2
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_n_estimators5
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_n_jobsnull
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_oob_scorefalse
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_random_state33003
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_verbose0
TESTf52a171b20sklearn.ensemble._forest.RandomForestClassifier(1)_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