Run
10559892

Run 10559892

Task 37 (Supervised Classification) diabetes Uploaded 21-05-2021 by Daniel Muller
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  • openml-python Sklearn_0.22.2.post1.
Issue #Downvotes for this reason By


Flow

sklearn.ensemble._forest.RandomForestClassifier(9)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(9)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(9)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(9)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(9)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(9)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(9)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(9)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(9)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(9)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(9)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(9)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(9)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(9)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(9)_random_state6862
sklearn.ensemble._forest.RandomForestClassifier(9)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(9)_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

0.8276 ± 0.0428
Per class
Cross-validation details (10-fold Crossvalidation)
0.7645 ± 0.0531
Per class
Cross-validation details (10-fold Crossvalidation)
0.4741 ± 0.1194
Cross-validation details (10-fold Crossvalidation)
0.3166 ± 0.0622
Cross-validation details (10-fold Crossvalidation)
0.3162 ± 0.0228
Cross-validation details (10-fold Crossvalidation)
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.7695 ± 0.0506
Cross-validation details (10-fold Crossvalidation)
768
Per class
Cross-validation details (10-fold Crossvalidation)
0.7643 ± 0.0553
Per class
Cross-validation details (10-fold Crossvalidation)
0.7695 ± 0.0506
Cross-validation details (10-fold Crossvalidation)
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.6957 ± 0.0502
Cross-validation details (10-fold Crossvalidation)
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.3984 ± 0.0266
Cross-validation details (10-fold Crossvalidation)
0.8358 ± 0.0564
Cross-validation details (10-fold Crossvalidation)
0.7286 ± 0.0584
Cross-validation details (10-fold Crossvalidation)