Run
18020

Run 18020

Task 119 (Supervised Classification) diabetes Uploaded 12-11-2019 by Continuous Integration
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  • openml-python Sklearn_0.22.dev0.
Issue #Downvotes for this reason By


Flow

sklearn.ensemble._forest.RandomForestClassifier(1)A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).
sklearn.ensemble._forest.RandomForestClassifier(1)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(1)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(1)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(1)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(1)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_n_estimators33
sklearn.ensemble._forest.RandomForestClassifier(1)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(1)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(1)_random_state55209
sklearn.ensemble._forest.RandomForestClassifier(1)_verbose0
sklearn.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.

18 Evaluation measures

0.8013
Per class
Cross-validation details (10% Holdout set)
0.7391
Per class
Cross-validation details (10% Holdout set)
0.4363
Cross-validation details (10% Holdout set)
0.3119
Cross-validation details (10% Holdout set)
0.3179
Cross-validation details (10% Holdout set)
0.4589
Cross-validation details (10% Holdout set)
0.7391
Cross-validation details (10% Holdout set)
253
Per class
Cross-validation details (10% Holdout set)
0.7391
Per class
Cross-validation details (10% Holdout set)
0.7391
Cross-validation details (10% Holdout set)
0.9463
Cross-validation details (10% Holdout set)
0.6927
Cross-validation details (10% Holdout set)
0.4813
Cross-validation details (10% Holdout set)
0.4157
Cross-validation details (10% Holdout set)
0.8638
Cross-validation details (10% Holdout set)
0.7182
Cross-validation details (10% Holdout set)