OpenML
7507

Run 7507

Task 259 (Supervised Classification) collins Uploaded 18-01-2024 by Test Test
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python Sklearn_1.3.2. study_619
Issue #Downvotes for this reason By


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_state16921
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
0.9935 ± 0.0031
Per class
F measure
0.8561 ± 0.0673
Per class
Kappa
0.8496 ± 0.0531
KB relative information score
0.6616 ± 0.02
Mean absolute error
0.0703 ± 0.0023
Mean prior absolute error
0.1212 ± 0.0002
Weighted recall
0.864 ± 0.0479
Number of instances
500
Per class
Precision
0.8616 ± 0.0534
Per class
Predictive accuracy
0.864 ± 0.0479
Prior entropy
3.6489 ± 0.0337
Relative absolute error
0.5805 ± 0.0193
Root mean prior squared error
0.246 ± 0.0003
Root mean squared error
0.1594 ± 0.0054
Root relative squared error
0.6477 ± 0.0222
Unweighted recall
0.7577 ± 0.0479