Scikit-Learn and Regression Error Metrics

You are going to learn a quick overview of Sicikit-Learn in this lesson. A library called Scikit-Learn contains a variety of machine learning algorithms. Using what they refer to as a generic estimate API framework to call and use the many models you will be creating is the most crucial aspect of this toolkit.

Linear Regression in Scikit-Learn

Regression Error Metrics

In regression problems, we have to use metrics designed for continuous values. Regression error metrics inform us about how much the actual values deviate from the regression line, which we estimate.

In this lesson, you will learn some of the most common evaluation metrics for regression:

• Mean Absolute Error: The mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. It is thus an arithmetic average of the absolute errors |ei| = |yi — xi|, where yi is the prediction and xi the true value.
• Mean Squared Error: The mean squared error (MSE) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors — that is, the average squared difference between the estimated values and the actual value.
• Root Mean Square Error: The root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed.

Photo on Make a Meme