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 known as the intercept...