Ways to Evaluate your Regression Model

The different methods | Data Series | Episode 10.1

After building a machine learning model whether that is using simple linear regression or gradient boosting it is important to get an idea of just how well your model performs.

This article is designed to give you an overview of some of the most common model evaluation methods for regression models along with their advantages and disadvantages.

Regression Problems

For regression problems, where we are building a model to predict numbers such as a house price or temperature, we can use the following metrics to evaluate our model:

Mean Squared Error (MSE)

The mean squared error takes the sum of squared distances between the model’s predicted values and actual values and divides this sum by the number of test examples.

The MSE takes the following formula:

Where:

To demonstrate how the mean squared errors is calculated: