Run
6560

Run 6560

Task 119 (Supervised Classification) diabetes Uploaded 12-11-2024 by Continuous Integration
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pip eline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._c lasses.DecisionTreeClassifier))(6)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.
sklearn.tree._classes.DecisionTreeClassifier(5)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(5)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(5)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(5)_max_depth1
sklearn.tree._classes.DecisionTreeClassifier(5)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(5)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(5)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(5)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(5)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(5)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(5)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(5)_presort"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(5)_random_state27485
sklearn.tree._classes.DecisionTreeClassifier(5)_splitter"best"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier)(5)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier)(5)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier)(5)_verbosefalse
sklearn.impute._base.SimpleImputer(5)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(5)_copytrue
sklearn.impute._base.SimpleImputer(5)_fill_valuenull
sklearn.impute._base.SimpleImputer(5)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(5)_strategy"mean"
sklearn.impute._base.SimpleImputer(5)_verbose0
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_cvnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_error_scoreNaN
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_iid"deprecated"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_n_jobsnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_param_grid{"classifier__max_depth": [1, 2, 3, 4, 5], "imputer__strategy": ["mean", "median"]}
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_refittrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_return_train_scorefalse
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_scoringnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier))(6)_verbose0

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