OpenML
1076

Run 1076

Task 259 (Supervised Classification) collins Uploaded 11-01-2024 by Test Test
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  • openml-python Sklearn_1.3.2. study_115
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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. 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(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"sqrt"
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_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_state12428
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
0.9949 ± 0.003
Per class
F measure
0.8857 ± 0.0408
Per class
Kappa
0.8807 ± 0.0516
KB relative information score
0.6741 ± 0.0176
Mean absolute error
0.0689 ± 0.0022
Mean prior absolute error
0.1212 ± 0.0002
Weighted recall
0.892 ± 0.0464
Number of instances
500
Per class
Precision
0.8909 ± 0.0358
Per class
Predictive accuracy
0.892 ± 0.0464
Prior entropy
3.6489 ± 0.0337
Relative absolute error
0.5689 ± 0.0186
Root mean prior squared error
0.246 ± 0.0003
Root mean squared error
0.1561 ± 0.0049
Root relative squared error
0.6346 ± 0.0201
Unweighted recall
0.7947 ± 0.0479