Run
145

Run 145

Task 119 (Supervised Classification) diabetes Uploaded 29-10-2019 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.tr ee.DecisionTreeClassifier))(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.
sklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(1)_max_depth1
sklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(1)_random_state2417
sklearn.tree.tree.DecisionTreeClassifier(1)_splitter"best"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(1)_cv"warn"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(1)_error_score"raise-deprecating"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(1)_iid"warn"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(1)_n_jobsnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(1)_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.tree.DecisionTreeClassifier))(1)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(1)_refittrue
>> clf = GridSearchCV(svc, parameters, cv=5) >>> clf.fit(iris.data, iris.target) ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS GridSearchCV(cv=5, error_score=..., estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., decision_function_shape='ovr', degree=..., gamma=..., kernel='...">sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(1)_return_train_scorefalse
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(1)_scoringnull
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(1)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_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.tree.DecisionTreeClassifier)(1)_verbosefalse
sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(1)_copytrue
sklearn.impute._base.SimpleImputer(1)_fill_valuenull
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"mean"
sklearn.impute._base.SimpleImputer(1)_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

0.7888
Per class
Cross-validation details (10% Holdout set)
0.719
Per class
Cross-validation details (10% Holdout set)
0.3922
Cross-validation details (10% Holdout set)
0.2998
Cross-validation details (10% Holdout set)
0.3206
Cross-validation details (10% Holdout set)
0.4589
Cross-validation details (10% Holdout set)
0.7194
Cross-validation details (10% Holdout set)
253
Per class
Cross-validation details (10% Holdout set)
0.7187
Per class
Cross-validation details (10% Holdout set)
0.7194
Cross-validation details (10% Holdout set)
0.9463
Cross-validation details (10% Holdout set)
0.6985
Cross-validation details (10% Holdout set)
0.4813
Cross-validation details (10% Holdout set)
0.4234
Cross-validation details (10% Holdout set)
0.8799
Cross-validation details (10% Holdout set)
0.6957
Cross-validation details (10% Holdout set)