Run
10560450

Run 10560450

Task 146824 (Supervised Classification) mfeat-pixel Uploaded 14-08-2021 by Sergey Redyuk
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Flow

sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn. tree.tree.ExtraTreeClassifier)(2)An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases. This class implements the algorithm known as AdaBoost-SAMME [2].
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_state61795
sklearn.tree.tree.ExtraTreeClassifier(28)_splitter"random"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.ExtraTreeClassifier)(2)_algorithm"SAMME.R"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.ExtraTreeClassifier)(2)_learning_rate0.003
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.ExtraTreeClassifier)(2)_n_estimators512
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.ExtraTreeClassifier)(2)_random_state3

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
0.9092 ± 0.0117
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8365 ± 0.0206
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8183 ± 0.0234
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.829 ± 0.0221
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0327 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8365 ± 0.0211
Cross-validation details (10-fold Crossvalidation)
Number of instances
2000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8371 ± 0.0184
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8365 ± 0.0211
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3219
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1817 ± 0.0234
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1808 ± 0.0118
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6028 ± 0.0395
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8365 ± 0.0211
Cross-validation details (10-fold Crossvalidation)