OpenML
10594049

Run 10594049

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 08-08-2023 by Jan van Rijn
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.ensemble._forest.RandomForestClassifier(32)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.
sklearn.ensemble._forest.RandomForestClassifier(32)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(32)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(32)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(32)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(32)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(32)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(32)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_n_estimators64
sklearn.ensemble._forest.RandomForestClassifier(32)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(32)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(32)_random_state39831
sklearn.ensemble._forest.RandomForestClassifier(32)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(32)_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
0.9826 ± 0.0034
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.929 ± 0.0058
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8563 ± 0.0117
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.635 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2033 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9292 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9298 ± 0.0057
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9292 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4109 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2635 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5298 ± 0.0102
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9261 ± 0.006
Cross-validation details (10-fold Crossvalidation)