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.
-
Notifications
You must be signed in to change notification settings - Fork 0
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
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
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.
Topics
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published