Run
9281

Run 9281

Task 119 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pip eline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.t ree.tree.DecisionTreeClassifier))(4)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.tree.DecisionTreeClassifier(8)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(8)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(8)_max_depth1
sklearn.tree.tree.DecisionTreeClassifier(8)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(8)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(8)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(8)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(8)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(8)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(8)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(8)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(8)_random_state9643
sklearn.tree.tree.DecisionTreeClassifier(8)_splitter"best"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_cvnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_error_score"raise"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_fit_paramsnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_iidtrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_n_jobs1
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_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.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_refittrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_return_train_score"warn"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_scoringnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(4)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(4)_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.preprocessing.imputation.Imputer(4)_axis0
sklearn.preprocessing.imputation.Imputer(4)_copytrue
sklearn.preprocessing.imputation.Imputer(4)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(4)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(4)_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