Run
10560369

Run 10560369

Task 9957 (Supervised Classification) qsar-biodeg 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_state60249
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_state11706

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
0.7949 ± 0.0494
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8116 ± 0.0412
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.5818 ± 0.0933
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5559 ± 0.0966
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1896 ± 0.0411
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4472 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8104 ± 0.0411
Cross-validation details (10-fold Crossvalidation)
Number of instances
1055
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8135 ± 0.0426
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8104 ± 0.0411
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9223 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4239 ± 0.0921
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4728 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4354 ± 0.0482
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9208 ± 0.1024
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7949 ± 0.0494
Cross-validation details (10-fold Crossvalidation)