The performance of a regression model can be evaluated using a number of metrics, including:
- Mean squared error (MSE): This is the average squared difference between the predicted values and the actual values.
- Mean absolute error (MAE): This is the average absolute difference between the predicted values and the actual values.
- Root mean squared error (RMSE): This is the square root of the mean squared error.
- R-squared: This is a measure of how well the model fits the data. It ranges from 0 to 1, with 1 being a perfect fit.
The best metric to use depends on the specific application. For example, if the cost of making a prediction error is high, then MSE or RMSE may be a better choice than MAE. If the goal is to make predictions that are as close to the actual values as possible, then MAE may be a better choice.
