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Table 2 Classifier performance statistics

From: VarSight: prioritizing clinically reported variants with binary classification algorithms

Classifier CV10 Acc. AUROC AUPRC
RandomForest(sklearn) 0.84+-0.13 0.9282 0.1961
LogisticRegression(sklearn) 0.84+-0.13 0.9300 0.2458
BalancedRandomForest(imblearn) 0.86+-0.11 0.9313 0.2015
EasyEnsembleClassifier(imblearn) 0.85+-0.08 0.9303 0.1918
  1. For each tuned classifier, we show performance measures commonly used for classifiers (from left to right): 10-fold cross validation balanced accuracy (CV10 Acc.), area under the receiver operator curve (AUROC), and area under the precision-recall curve (AUPRC). The CV10 Acc. was gathered during hyperparameter tuning by calculating the average and standard deviation of the 10-fold cross validation. AUROC and AUPRC was evaluated on the testing set after hyperparameter tuning and fitting to the full training set