In my previous posts, I have covered some regression models (simple linear regression, polynomial regression) and classification models (k-nearest neighbors, support vector machines). However, I haven’t really discussed in-depth different ways to evaluate these models. Without proper metrics, not only can you not claim the accuracy of your models confidently but you also cannot compare different models to pick the most accurate one.
In this post, I want to focus on some of the most popular metrics that are used to evaluate regression models. These metrics are (in no particular order):
- Explained Variance Score (EVS)
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- R Squared Score (R2 Score)
- Adjusted R Squared Score
These metrics were calculated in my post (except for adjusted R2 score) about implementing polynomial regression model.