Run
10560090

Run 10560090

Task 167119 (Supervised Classification) jungle_chess_2pcs_raw_endgame_complete Uploaded 13-08-2021 by Sergey Redyuk
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.ensemble.forest.RandomForestClassifier(66)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 use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).
sklearn.ensemble.forest.RandomForestClassifier(66)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(66)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(66)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(66)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(66)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(66)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(66)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(66)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(66)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(66)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(66)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(66)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(66)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(66)_random_state43614
sklearn.ensemble.forest.RandomForestClassifier(66)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(66)_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
0.9123 ± 0.0036
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7834 ± 0.0055
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6229 ± 0.0098
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6179 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1585 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3832 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7848 ± 0.0059
Cross-validation details (10-fold Crossvalidation)
Number of instances
44819
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7823 ± 0.0053
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7848 ± 0.0059
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.3491 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4136 ± 0.006
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4377 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3172 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7247 ± 0.0093
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7083 ± 0.0056
Cross-validation details (10-fold Crossvalidation)