Run
4648

Run 4648

Task 119 (Supervised Classification) diabetes Uploaded 18-10-2024 by Continuous Integration
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Flow

sklearn.ensemble._forest.RandomForestClassifier(10)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(10)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(10)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(10)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(10)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(10)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(10)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(10)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(10)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(10)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(10)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(10)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(10)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(10)_monotonic_cstnull
sklearn.ensemble._forest.RandomForestClassifier(10)_n_estimators33
sklearn.ensemble._forest.RandomForestClassifier(10)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(10)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(10)_random_state17308
sklearn.ensemble._forest.RandomForestClassifier(10)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(10)_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
0.8248
Per class
F measure
0.7603
Per class
Kappa
0.4778
KB relative information score
0.327
Mean absolute error
0.3133
Mean prior absolute error
0.4589
Weighted recall
0.7628
Number of instances
253
Per class
Precision
0.7594
Per class
Predictive accuracy
0.7628
Prior entropy
0.9463
Relative absolute error
0.6828
Root mean prior squared error
0.4813
Root mean squared error
0.4029
Root relative squared error
0.8371
Unweighted recall
0.7345