OpenML
9029

Run 9029

Task 1196 (Supervised Classification) iris Uploaded 01-03-2024 by Test Test
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Flow

sklearn.ensemble._forest.ExtraTreesClassifier(1)An extra-trees classifier. This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
sklearn.ensemble._forest.ExtraTreesClassifier(1)_bootstrapfalse
sklearn.ensemble._forest.ExtraTreesClassifier(1)_ccp_alpha0.0
sklearn.ensemble._forest.ExtraTreesClassifier(1)_class_weightnull
sklearn.ensemble._forest.ExtraTreesClassifier(1)_criterion"gini"
sklearn.ensemble._forest.ExtraTreesClassifier(1)_max_depthnull
sklearn.ensemble._forest.ExtraTreesClassifier(1)_max_features"sqrt"
sklearn.ensemble._forest.ExtraTreesClassifier(1)_max_leaf_nodesnull
sklearn.ensemble._forest.ExtraTreesClassifier(1)_max_samplesnull
sklearn.ensemble._forest.ExtraTreesClassifier(1)_min_impurity_decrease0.0
sklearn.ensemble._forest.ExtraTreesClassifier(1)_min_samples_leaf1
sklearn.ensemble._forest.ExtraTreesClassifier(1)_min_samples_split2
sklearn.ensemble._forest.ExtraTreesClassifier(1)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.ExtraTreesClassifier(1)_n_estimators100
sklearn.ensemble._forest.ExtraTreesClassifier(1)_n_jobsnull
sklearn.ensemble._forest.ExtraTreesClassifier(1)_oob_scorefalse
sklearn.ensemble._forest.ExtraTreesClassifier(1)_random_state62565
sklearn.ensemble._forest.ExtraTreesClassifier(1)_verbose0
sklearn.ensemble._forest.ExtraTreesClassifier(1)_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
0.9966 ± 0.0056
Per class
F measure
0.9533 ± 0.0464
Per class
Kappa
0.93 ± 0.0675
KB relative information score
0.9208 ± 0.048
Mean absolute error
0.0422 ± 0.0222
Mean prior absolute error
0.4444
Weighted recall
0.9533 ± 0.045
Number of instances
150
Per class
Precision
0.9534 ± 0.0343
Per class
Predictive accuracy
0.9533 ± 0.045
Prior entropy
1.585
Relative absolute error
0.095 ± 0.0499
Root mean prior squared error
0.4714
Root mean squared error
0.1445 ± 0.0715
Root relative squared error
0.3064 ± 0.1517
Unweighted recall
0.9533 ± 0.045