The objective of my experiment is to analyze the performance of Random Forest, Naive Bayes, Logistic Regression, and K-nearest neighbors machine learning models for evaluation of the utility of synthesized data for fraud detection. I used the accuracy, AUROC, precision, recall, and f1-scores performance metrics to evaluate the model’s performance.
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The objective of my experiment is to analyze the performance of Random Forest, Naive Bayes, Logistic Regression, and K-nearest neighbors machine learning models for evaluation of the utility of synthesized data for fraud detection.
AliValiyev/Evaluation-the-quality-of-synthetic-data-set-for-fraud-detection
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The objective of my experiment is to analyze the performance of Random Forest, Naive Bayes, Logistic Regression, and K-nearest neighbors machine learning models for evaluation of the utility of synthesized data for fraud detection.
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