Run
10560490

Run 10560490

Task 9985 (Supervised Classification) first-order-theorem-proving Uploaded 14-08-2021 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_state46309
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
0.72 ± 0.0101
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5526 ± 0.0154
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4049 ± 0.0207
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.4204 ± 0.0187
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1508 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2508 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5495 ± 0.0164
Cross-validation details (10-fold Crossvalidation)
Number of instances
6118
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5562 ± 0.0144
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5495 ± 0.0164
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.3 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6012 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3541 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3843 ± 0.0068
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0853 ± 0.0194
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4577 ± 0.0186
Cross-validation details (10-fold Crossvalidation)