Run
10591885

Run 10591885

Task 119 (Learning Curve) zoo Uploaded 10-03-2023 by Artur Dev
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Flow

sklearn.ensemble._forest.RandomForestClassifier(25)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(25)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(25)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(25)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(25)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(25)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(25)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(25)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(25)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(25)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(25)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(25)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(25)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(25)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(25)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(25)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(25)_random_state62148
sklearn.ensemble._forest.RandomForestClassifier(25)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(25)_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
0.999 ± 0.0046
Per class
F measure
0.9639
Per class
Kappa
0.9541 ± 0.0796
KB relative information score
0.9249 ± 0.0599
Mean absolute error
0.0235 ± 0.0137
Mean prior absolute error
0.2188 ± 0.0049
Weighted recall
0.9653 ± 0.0595
Number of instances
1010
Per class
Precision
0.965
Per class
Predictive accuracy
0.9653 ± 0.0595
Prior entropy
2.3926 ± 0.137
Relative absolute error
0.1076 ± 0.0616
Root mean prior squared error
0.3294 ± 0.0075
Root mean squared error
0.0862 ± 0.0421
Root relative squared error
0.2616 ± 0.1261
Unweighted recall
0.9079 ± 0.138