Machine Learning for OpenCV 4 - Second Edition

Understanding linear regression

The easiest regression model is called linear regression. The idea behind linear regression is to describe a target variable (such as Boston house pricing—recall the various datasets we studied in Chapter 1, A Taste of Machine Learning) with a linear combination of features.

To keep things simple, let's just focus on two features. Let's say we want to predict tomorrow's stock prices using two features: today's stock price and yesterday's stock price. We will denote today's stock price as the first feature, f₁, and yesterday's stock price as f₂. Then, the goal of linear regression would be to learn two weight coefficients, w₁ and w₂, so that we can predict tomorrow's stock price as follows:

Here, is the