Run
10708

Run 10708

Task 115 (Supervised Classification) diabetes Uploaded 29-11-2022 by Test Test
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  • openml-python Sklearn_1.1.2. study_2303
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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 controlled with the `max_samples` parameter if `bootstrap=True` (default), otherwise the whole dataset is used to build each tree.
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"sqrt"
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_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_state33852
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

Area under ROC curve
0.8297 ± 0.0499
Per class
F measure
0.7635 ± 0.0553
Per class
Kappa
0.4738 ± 0.1241
KB relative information score
0.3158 ± 0.0684
Mean absolute error
0.3169 ± 0.0254
Mean prior absolute error
0.4545 ± 0.0011
Weighted recall
0.7669 ± 0.0535
Number of instances
768
Per class
Precision
0.7625 ± 0.0565
Per class
Predictive accuracy
0.7669 ± 0.0535
Prior entropy
0.9331 ± 0.0032
Relative absolute error
0.6972 ± 0.0562
Root mean prior squared error
0.4766 ± 0.0011
Root mean squared error
0.3979 ± 0.0288
Root relative squared error
0.8347 ± 0.0611
Unweighted recall
0.731 ± 0.0613