Run
10560055

Run 10560055

Task 3549 (Supervised Classification) analcatdata_authorship 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_state11236
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
0.9986 ± 0.0015
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9761 ± 0.0198
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9653 ± 0.0271
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8512 ± 0.0255
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0743 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3439 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9762 ± 0.0185
Cross-validation details (10-fold Crossvalidation)
Number of instances
841
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9763 ± 0.0176
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9762 ± 0.0185
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.7874 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2161 ± 0.0325
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4146 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1433 ± 0.0164
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3456 ± 0.0395
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9581 ± 0.0462
Cross-validation details (10-fold Crossvalidation)