Run
10587727

Run 10587727

Task 119 (Learning Curve) zoo Uploaded 16-03-2022 by VASILEIOS GARGANOURAKIS
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Flow

sklearn.ensemble._forest.RandomForestClassifier(11)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(11)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(11)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(11)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(11)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(11)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(11)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(11)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(11)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(11)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(11)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(11)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(11)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(11)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(11)_random_state18202
sklearn.ensemble._forest.RandomForestClassifier(11)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(11)_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
0.9993 ± 0.0034
Per class
F measure
0.9588
Per class
Kappa
0.949 ± 0.0786
KB relative information score
0.9268 ± 0.0526
Mean absolute error
0.0239 ± 0.0133
Mean prior absolute error
0.2188 ± 0.0049
Weighted recall
0.9614 ± 0.0593
Number of instances
1010
Per class
Precision
0.9599
Per class
Predictive accuracy
0.9614 ± 0.0593
Prior entropy
2.3926 ± 0.137
Relative absolute error
0.1091 ± 0.0598
Root mean prior squared error
0.3294 ± 0.0075
Root mean squared error
0.0856 ± 0.04
Root relative squared error
0.2598 ± 0.1196
Unweighted recall
0.8979 ± 0.0999