Run
160

Run 160

Task 119 (Supervised Classification) diabetes Uploaded 29-10-2019 by Continuous Integration
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TEST2c75fb43c3sklearn.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.
TEST2c75fb43c3sklearn.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"}}}
TEST2c75fb43c3sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_error_score"raise-deprecating"
TEST2c75fb43c3sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_iid"warn"
TEST2c75fb43c3sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_jobsnull
TEST2c75fb43c3sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_param_grid[{"max_features": [2, 4]}, {"min_samples_leaf": [1, 10]}]
TEST2c75fb43c3sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_pre_dispatch"2*n_jobs"
TEST2c75fb43c3sklearn.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_score=..., estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., decision_function_shape='ovr', degree=..., gamma=..., kernel='...">TEST2c75fb43c3sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_return_train_scorefalse
TEST2c75fb43c3sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_scoringnull
TEST2c75fb43c3sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose0
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_bootstraptrue
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_class_weightnull
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_criterion"gini"
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_max_depthnull
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_max_features"auto"
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_max_leaf_nodesnull
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_decrease0.0
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_splitnull
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_leaf1
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_split2
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_n_estimators5
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_n_jobsnull
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_oob_scorefalse
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_random_state33003
TEST2c75fb43c3sklearn.ensemble.forest.RandomForestClassifier(1)_verbose0
TEST2c75fb43c3sklearn.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

0.7669
Per class
Cross-validation details (10% Holdout set)
0.7183
Per class
Cross-validation details (10% Holdout set)
0.3894
Cross-validation details (10% Holdout set)
0.2792
Cross-validation details (10% Holdout set)
0.3296
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)
0.7175
Per class
Cross-validation details (10% Holdout set)
0.7194
Cross-validation details (10% Holdout set)
0.9463
Cross-validation details (10% Holdout set)
0.7183
Cross-validation details (10% Holdout set)
0.4813
Cross-validation details (10% Holdout set)
0.4339
Cross-validation details (10% Holdout set)
0.9017
Cross-validation details (10% Holdout set)
0.6933
Cross-validation details (10% Holdout set)