About this document

Software Defect Prediction Model Metrics by Hdyb is a document available to read on EtoBox.

This document compares the performance of various machine learning models for software defect prediction on various evaluation metrics including accuracy, F1 score, recall, precision, and AUC. It finds that random forest and the proposed boosted voting classifier have the best performance with accuracies above 85%. KNN and decision tree also perform well with accuracies over 75% and 80% respectively, while Gaussian naive Bayes has the lowest accuracy around 57%.

Author

Hdyb

Language

EN