Run
10595190

Run 10595190

Task 7543 (Supervised Classification) diabetes Uploaded 03-12-2024 by Vitor Pina
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Flow

sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.svm. _classes.SVC)(1)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 based on [2]_.
sklearn.svm._classes.SVC(17)_C1.0
sklearn.svm._classes.SVC(17)_break_tiesfalse
sklearn.svm._classes.SVC(17)_cache_size200
sklearn.svm._classes.SVC(17)_class_weightnull
sklearn.svm._classes.SVC(17)_coef00.0
sklearn.svm._classes.SVC(17)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(17)_degree3
sklearn.svm._classes.SVC(17)_gamma"scale"
sklearn.svm._classes.SVC(17)_kernel"rbf"
sklearn.svm._classes.SVC(17)_max_iter-1
sklearn.svm._classes.SVC(17)_probabilitytrue
sklearn.svm._classes.SVC(17)_random_state18243
sklearn.svm._classes.SVC(17)_shrinkingtrue
sklearn.svm._classes.SVC(17)_tol0.001
sklearn.svm._classes.SVC(17)_verbosefalse
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.svm._classes.SVC)(1)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.svm._classes.SVC)(1)_learning_rate1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.svm._classes.SVC)(1)_n_estimators50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.svm._classes.SVC)(1)_random_state25854

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
0.6791
Per class
Cross-validation details (10% Holdout set)
F measure
0.5695
Per class
Cross-validation details (10% Holdout set)
Kappa
0.0893
Cross-validation details (10% Holdout set)
KB relative information score
0.031
Cross-validation details (10% Holdout set)
Mean absolute error
0.4455
Cross-validation details (10% Holdout set)
Mean prior absolute error
0.4589
Cross-validation details (10% Holdout set)
Weighted recall
0.6482
Cross-validation details (10% Holdout set)
Number of instances
253
Per class
Cross-validation details (10% Holdout set)
Precision
0.6247
Per class
Cross-validation details (10% Holdout set)
Predictive accuracy
0.6482
Cross-validation details (10% Holdout set)
Prior entropy
0.9463
Cross-validation details (10% Holdout set)
Relative absolute error
0.9707
Cross-validation details (10% Holdout set)
Root mean prior squared error
0.4813
Cross-validation details (10% Holdout set)
Root mean squared error
0.4759
Cross-validation details (10% Holdout set)
Root relative squared error
0.9889
Cross-validation details (10% Holdout set)
Unweighted recall
0.5373
Cross-validation details (10% Holdout set)