Run
10594045

Run 10594045

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 08-08-2023 by Jan van Rijn
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Flow

sklearn.ensemble._forest.RandomForestClassifier(34)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(34)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(34)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(34)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(34)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(34)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(34)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(34)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(34)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(34)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(34)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(34)_n_estimators64
sklearn.ensemble._forest.RandomForestClassifier(34)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(34)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(34)_random_state7021
sklearn.ensemble._forest.RandomForestClassifier(34)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(34)_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
0.9827 ± 0.0031
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9294 ± 0.0048
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8571 ± 0.0097
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6365 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2025 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9296 ± 0.0048
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9302 ± 0.0051
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9296 ± 0.0048
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4094 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2628 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5284 ± 0.0084
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9266 ± 0.0046
Cross-validation details (10-fold Crossvalidation)