Run
10453298

Run 10453298

Task 119 (Learning Curve) zoo Uploaded 18-05-2020 by sri ranga sai
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  • openml-python Sklearn_0.22.2.post1.
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Flow

sklearn.ensemble._forest.RandomForestClassifier(3)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 always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).
sklearn.ensemble._forest.RandomForestClassifier(3)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(3)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(3)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(3)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(3)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(3)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(3)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(3)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(3)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(3)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(3)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(3)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(3)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(3)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(3)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(3)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(3)_random_state1974
sklearn.ensemble._forest.RandomForestClassifier(3)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(3)_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 ± 0.0026
Per class
F measure
0.9562
Per class
Kappa
0.9462 ± 0.0833
KB relative information score
0.9241 ± 0.0583
Mean absolute error
0.0238 ± 0.0136
Mean prior absolute error
0.2188 ± 0.0049
Weighted recall
0.9594 ± 0.0623
Number of instances
1010
Per class
Precision
0.9569
Per class
Predictive accuracy
0.9594 ± 0.0623
Prior entropy
2.3926 ± 0.137
Relative absolute error
0.1088 ± 0.0608
Root mean prior squared error
0.3294 ± 0.0075
Root mean squared error
0.0872 ± 0.0417
Root relative squared error
0.2647 ± 0.1246
Unweighted recall
0.8921 ± 0.1355