With the wealth of data brought by the digital age, the business world is focusing on gaining valuable insights from complex data sets. Navigating this big data ocean has become the key to gaining competitive advantage and creating the right strategies. This is exactly where regression models come into play. As a powerful branch of data science and statistics, regression encompasses a number of important applications, from predicting the future to analyzing market trends. However, evaluating the success of regression models is vital to obtaining accurate results. In this blog post, we will explore the importance of evaluating success in regression models and what the metrics used mean.
Evaluating Success in Regression Models
MSE (Mean Square Error)
MSE, that is, Mean Square Error, is calculated by dividing the sum of the squares of the differences between the actual values and the predicted values by the number of observations. In this metric, squaring the errors prevents positive and negative values from canceling each other, allowing us to measure the magnitude of the errors more accurately. However, squaring may overscale the magnitude of errors. As a solution to this situation, RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) metrics come into play.
RMSE (Root Mean Square Error)
RMSE is used to avoid inflation in MSE and obtain more accurate measurements. Unlike MSE, the RMSE metric measures errors by squaring them and then taking their square root. In this way, the effect of large error values is more balanced.
MAE (Mean Absolute Error)
MAE serves a similar purpose to RMSE, but has a different approach. It evaluates the differences (i.e. error values) between actual values and predicted values in absolute value. This maintains the balance between negative and positive errors and also prevents the magnitude of errors from becoming overinflated. Therefore, these metrics are used to evaluate the predictive success of regression models and are important measures used to obtain accurate results.