Predicting continuous variables with linear regression
We will finally explore our first true ML model! Linear regression is a form of regression, which means that it is an ML model that attempts to find a relationship between predictors and a response variable, and that response variable is – you guessed it –continuous! This notion is synonymous with making a line of best fit. While linear regressions are no longer a state-of-the-art ML algorithm, the path behind it can be a bit tricky and it will serve as an excellent entry point for us.
In the case of linear regression, we will attempt to find a linear relationship between our predictors and our response variable. Formally, we wish to solve a formula of the following format:
Let’s look at the constituents of this formula:
- y is our response variable
- xi is our ith variable (ith column or ith predictor)
- B0 is the intercept
- Bi is the coefficient for the xi term
Let’s take...
