OpenML
7407

Run 7407

Task 115 (Supervised Classification) diabetes Uploaded 18-01-2024 by Test Test
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  • openml-python Sklearn_1.3.2. study_616
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Flow

sklearn.ensemble._forest.RandomForestClassifier(7)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. For a comparison between tree-based ensemble models see the example :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.
sklearn.ensemble._forest.RandomForestClassifier(7)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(7)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(7)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(7)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(7)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(7)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(7)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(7)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(7)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(7)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(7)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(7)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(7)_random_state47273
sklearn.ensemble._forest.RandomForestClassifier(7)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(7)_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
0.8291 ± 0.0422
Per class
F measure
0.7654 ± 0.0462
Per class
Kappa
0.4757 ± 0.1028
KB relative information score
0.3124 ± 0.0556
Mean absolute error
0.3184 ± 0.0204
Mean prior absolute error
0.4545 ± 0.0011
Weighted recall
0.7708 ± 0.0451
Number of instances
768
Per class
Precision
0.7655 ± 0.0504
Per class
Predictive accuracy
0.7708 ± 0.0451
Prior entropy
0.9331 ± 0.0032
Relative absolute error
0.7005 ± 0.0451
Root mean prior squared error
0.4766 ± 0.0011
Root mean squared error
0.3976 ± 0.0231
Root relative squared error
0.8342 ± 0.0494
Unweighted recall
0.7288 ± 0.0495