Run
9392

Run 9392

Task 119 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
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TEST65f9f4bb94sklearn.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 "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.
TEST65f9f4bb94sklearn.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"}}}
TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_error_score"raise-deprecating"
TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_fit_paramsnull
TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_iid"warn"
TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_jobsnull
TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_param_grid[{"max_features": [2, 4]}, {"min_samples_leaf": [1, 10]}]
TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_refittrue
>> clf = GridSearchCV(svc, parameters, cv=5) >>> clf.fit(iris.data, iris.target) ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS GridSearchCV(cv=5, error_...">TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_return_train_score"warn"
TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_scoringnull
TEST65f9f4bb94sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose0
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_bootstraptrue
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_class_weightnull
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_criterion"gini"
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_max_depthnull
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_max_features"auto"
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_max_leaf_nodesnull
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_decrease0.0
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_splitnull
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_leaf1
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_split2
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_n_estimators5
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_n_jobsnull
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_oob_scorefalse
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_random_state33003
TEST65f9f4bb94sklearn.ensemble.forest.RandomForestClassifier(1)_verbose0
TEST65f9f4bb94sklearn.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