A common question by beginners to regression predictive modeling projects is:
How do I calculate accuracy for my regression model?
Accuracy (e.g. classification accuracy) is a measure for classification, not regression.
We cannot calculate accuracy for a regression model.
The skill or performance of a regression model must be reported as an error in those predictions.
This makes sense if you think about it. If you are predicting a numeric value like a height or a dollar amount, you don’t want to know if the model predicted the value exactly (this might be intractably difficult in practice); instead, we want to know how close the predictions were to the expected values.
Error addresses exactly this and summarizes on average how close predictions were to their expected values.
There are three error metrics that are commonly used for evaluating and reporting the performance of a regression model; they are:
- Mean Squared Error (MSE).
- Root Mean Squared Error (RMSE).
- Mean Absolute Error (MAE)
There are many other metrics for regression, although these are the most commonly used. You can see the full list of regression metrics supported by the scikit-learn Python machine learning library here: