Introduction to linear regression

In linear regression, the output variable is predicted by a linearly weighted combination of input features. Here is an example of a simple linear model:

The preceding model essentially says that we are estimating one output, denoted by , and this is a linear function of a single predictor variable (that is, a feature) denoted by the letter x. The terms involving the Greek letter β are the parameters of the model and are known as regression coefficients. Once we train the model and settle on values for these parameters, we can make a prediction on the output variable for any value of x by a simple substitution in our equation. Another example of a linear model, this time with three features and with values assigned to the regression coefficients, is given by the following equation:

In this equation, just as with the previous one, we can observe that we have one more coefficient than the number of features. This additional coefficient, β0, is...