Optimizing Linear Regression Models: A Comparative Study of Error Metrics

A revolutionary area of artificial intelligence called machine learning enables computers to learn from data and forecast without the need for explicit programming. A key component of predictive modeling is linear regression, which examines the relationships between independent and dependent variables, is a crucial part of predictive modelling. In this work, a comparative evaluation of these error measures with an emphasis on linear regression model optimization. Our results show that the selection of error measures has a substantial impact on model optimization and evaluation, with different effects for different datasets and model configurations. Three separate datasets are included in the analysis: Iris, Wine Quality, and California Housing. We assess the four-error metrics' performance for every dataset. Because of its outlier sensitivity, MSE is a good choice for applications where outlier minimization is a top priority. Due to its ability to withstand outliers, MAE can be used with noisy datasets. RMSE routinely beats other metrics by finding a balance between robustness and precision. There are important practical ramifications: RMSE performs well when accurate forecasts are critical, whereas MAE is useful when outlier information is important. Because it minimizes the impact of outliers, mean square error (MSE) is especially appropriate for specific types of datasets. Our research provides insightful information about whether to use error measures to match objectives with features of the data. Selecting the appropriate assessment measure is essential for optimizing models and making decisions, increasing the precision of model assessment in practical scenarios. This research improves the accuracy of model evaluation across a range of real-world applications by providing practitioners with the ability to make well-informed decisions regarding error metric selection.