Run
9572

Run 9572

Task 115 (Supervised Classification) diabetes Uploaded 04-03-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_1262
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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 controlled with the `max_samples` parameter if `bootstrap=True` (default), otherwise the whole dataset is used to build each tree.
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_estimators100
sklearn.ensemble._forest.RandomForestClassifier(1)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(1)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(1)_random_state30062
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

Area under ROC curve
0.8311 ± 0.0429
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7651 ± 0.0458
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.476 ± 0.1034
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3151 ± 0.0602
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3168 ± 0.0226
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7695 ± 0.0439
Cross-validation details (10-fold Crossvalidation)
Number of instances
768
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7645 ± 0.0469
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7695 ± 0.0439
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6971 ± 0.05
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3967 ± 0.0255
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8323 ± 0.0541
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7304 ± 0.0502
Cross-validation details (10-fold Crossvalidation)