Machine Learning and AI for Absolute Beginners

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LOGISTIC REGRESSION

 

As demonstrated in the previous chapter, linear regression is useful for quantifying relationships between variables to predict a continuous outcome. Total bill and size (number of guests) are both examples of continuous variables.

However, what if we want to predict a categorical variable such as “new customer” or “returning customer”? Unlike linear regression, the dependent variable (y) is no longer a continuous variable (such as total tip) but rather a discrete categorical variable.

Rather than quantify the linear relationship between variables, we need to use a classification technique such as logistic regression.

Logistic regression is still a supervised learning technique but produces a qualitative ...