Run
10559970

Run 10559970

Task 10093 (Supervised Classification) banknote-authentication Uploaded 13-08-2021 by Sergey Redyuk
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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_state461
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
0.9998 ± 0.0003
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9949 ± 0.0049
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9897 ± 0.01
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9703 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0173 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4939 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9949 ± 0.0049
Cross-validation details (10-fold Crossvalidation)
Number of instances
1372
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9949 ± 0.0048
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9949 ± 0.0049
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9911 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.035 ± 0.0104
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4969 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0711 ± 0.0218
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.143 ± 0.0439
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9949 ± 0.0053
Cross-validation details (10-fold Crossvalidation)