Run
3250

Run 3250

Task 115 (Supervised Classification) diabetes Uploaded 18-10-2024 by Test Test
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  • openml-python Sklearn_1.5.2. study_168
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Flow

sklearn.ensemble._forest.RandomForestClassifier(2)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. Trees in the forest use the best split strategy, i.e. equivalent to passing `splitter="best"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`. 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(2)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_monotonic_cstnull
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state5671
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_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
0.8239 ± 0.0481
Per class
F measure
0.7604 ± 0.0455
Per class
Kappa
0.4661 ± 0.1028
KB relative information score
0.3099 ± 0.0672
Mean absolute error
0.3179 ± 0.0256
Mean prior absolute error
0.4545 ± 0.0011
Weighted recall
0.7643 ± 0.0438
Number of instances
768
Per class
Precision
0.7594 ± 0.0469
Per class
Predictive accuracy
0.7643 ± 0.0438
Prior entropy
0.9331 ± 0.0032
Relative absolute error
0.6994 ± 0.0565
Root mean prior squared error
0.4766 ± 0.0011
Root mean squared error
0.4008 ± 0.0282
Root relative squared error
0.8409 ± 0.0598
Unweighted recall
0.7264 ± 0.0503