Run
10591876

Run 10591876

Task 119 (Learning Curve) zoo Uploaded 21-02-2023 by Alaa Othman
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Flow

sklearn.ensemble._forest.RandomForestClassifier(24)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(24)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(24)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(24)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(24)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(24)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(24)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(24)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(24)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(24)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(24)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(24)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(24)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(24)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(24)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(24)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(24)_random_state33935
sklearn.ensemble._forest.RandomForestClassifier(24)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(24)_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
0.9991 ± 0.0044
Per class
F measure
0.9613
Per class
Kappa
0.9516 ± 0.0811
KB relative information score
0.9259 ± 0.0581
Mean absolute error
0.0237 ± 0.0137
Mean prior absolute error
0.2188 ± 0.0049
Weighted recall
0.9634 ± 0.0616
Number of instances
1010
Per class
Precision
0.9628
Per class
Predictive accuracy
0.9634 ± 0.0616
Prior entropy
2.3926 ± 0.137
Relative absolute error
0.1081 ± 0.0615
Root mean prior squared error
0.3294 ± 0.0075
Root mean squared error
0.0852 ± 0.0417
Root relative squared error
0.2587 ± 0.1247
Unweighted recall
0.9036 ± 0.1355