Run
10560409

Run 10560409

Task 146817 (Supervised Classification) steel-plates-fault Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn. tree.tree.DecisionTreeClassifier)(15)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.DecisionTreeClassifier(66)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(66)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(66)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(66)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(66)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(66)_min_impurity_split1e-07
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(66)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(66)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(66)_random_state61314
sklearn.tree.tree.DecisionTreeClassifier(66)_splitter"best"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(15)_algorithm"SAMME.R"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(15)_learning_rate1.0
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(15)_n_estimators50
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(15)_random_state56984

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
0.8238 ± 0.0179
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7119 ± 0.0281
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6295 ± 0.0361
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6776 ± 0.0334
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0824 ± 0.008
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7115 ± 0.0281
Cross-validation details (10-fold Crossvalidation)
Number of instances
1941
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.713 ± 0.0289
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7115 ± 0.0281
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.4107 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3707 ± 0.0359
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2871 ± 0.0139
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8612 ± 0.0414
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7405 ± 0.0384
Cross-validation details (10-fold Crossvalidation)