OpenML
10560136

Run 10560136

Task 23 (Supervised Classification) cmc Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.tree.tree.ExtraTreeClassifier(28)An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the `max_features` randomly selected features and the best split among those is chosen. When `max_features` is set 1, this amounts to building a totally random decision tree. Warning: Extra-trees should only be used within ensemble methods.
sklearn.tree.tree.ExtraTreeClassifier(28)_class_weightnull
sklearn.tree.tree.ExtraTreeClassifier(28)_criterion"gini"
sklearn.tree.tree.ExtraTreeClassifier(28)_max_depth1000
sklearn.tree.tree.ExtraTreeClassifier(28)_max_features"auto"
sklearn.tree.tree.ExtraTreeClassifier(28)_max_leaf_nodesnull
sklearn.tree.tree.ExtraTreeClassifier(28)_min_impurity_split1e-07
sklearn.tree.tree.ExtraTreeClassifier(28)_min_samples_leaf1
sklearn.tree.tree.ExtraTreeClassifier(28)_min_samples_split2
sklearn.tree.tree.ExtraTreeClassifier(28)_min_weight_fraction_leaf0.0
sklearn.tree.tree.ExtraTreeClassifier(28)_random_state17153
sklearn.tree.tree.ExtraTreeClassifier(28)_splitter"random"

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
0.5826 ± 0.0336
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4536 ± 0.0425
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1552 ± 0.0657
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2198 ± 0.0568
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3647 ± 0.0269
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.4535 ± 0.0424
Cross-validation details (10-fold Crossvalidation)
Number of instances
1473
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4538 ± 0.0431
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4535 ± 0.0424
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.539 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8465 ± 0.0624
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4641 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5958 ± 0.0236
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2838 ± 0.0508
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4349 ± 0.0425
Cross-validation details (10-fold Crossvalidation)