The challenge at hand and the objectives of the investigation should inform the choice of metrics best suited for assessing the effectiveness of a regression model. However, there are a few widely-used evaluation metrics for regression that are accepted as excellent indications of a model’s success.

## Mean Squared Error

A typical statistic for judging the quality of regression models is the Mean Squared Error (MSE). The average squared error between the target variable and its projected value is calculated. The better the model fits the data, the less the MSE score.

## Root Mean Squared Error

It is the square root of the average squared difference; RMSE is similar but takes the square root of the average squared difference. The fact that it is stated in the same units as the objective variable makes it a popular measure.

The average absolute difference between the anticipated and actual values of the target variable is known as the Mean Absolute Error (MAE). When comparing estimation methods, MAE is preferred since it is less likely to be affected by extreme values.

## R-squared

R2 is a statistical metric for how much of the variation in the dependent variable can be attributed to the model. It may take on values between 0 and 1, with a greater value suggesting a more precise match between the model and the data.

## Adjusted R-squared

The number of predictor variables is considered while calculating the adjusted R-squared, which is otherwise comparable to the R-squared. For assessing models with several predictor variables, this statistic is helpful.

## Mean Absolute Percentage Error

The mean absolute percentage error (MAPE) quantifies the average absolute difference between the anticipated and observed values of the target variable as a percentage of the observed value. The method works well for assessing models where the target variable spans a broad range of scales.

## COD

The Coefficient of Determination (COD) is a comparable statistic to R-squared that assesses how well the model’s predictions fit the data.

## Wrap up

The situation at hand and the aims of the investigation dictate the most appropriate metrics for regression models. Regression models are often evaluated using MSE, RMSE, MAE, R-squared, modified R-squared, MAPE, and COD. To thoroughly assess the model’s performance, it is advised to employ a mix of these regression model metrics.