Optimization of Bug Detection Model(OBDM):By Evaluating Performance Metric Using Artificial Intelligence

Machine learning is a growing field of computational algorithms that aim to simulate human intelligence by learning from their surroundings. There are many machines learning Models to validate the work like the Regression model. The Regression model is implemented in the JAVA dataset of bug, where 21 data metrics are available different performance metrics are used like R-squared (R2), Root Mean Squared Error (RMSE), Residual Standard Error (RSE), Mean Absolute Error (MAE). Adding more variables to the model may increase R2 and reduce RMSE, so some strong methods should be there which evaluate the select the model with minimum variables dependency. Akaike's Information Criteria acronym as AIC and BIC means Bayesian Information Criteria. Mallows' Cp, named after Colin Lingwood Mallows, is a measure of a regression model's fit. AIC, BIC, and Mallows' Cp help find the significant data metrics that help to evaluate and select the right model. The lower these metrics the better the model. This paper explains various statistical regression metrics for the performance measure of a regression model and also evaluated the average prediction error rate to get the optimal model selection.