# Summary

Go over a summary of what we have learned in this chapter.

In this chapter, we learned about the following concepts.

## Logistic regression

Logistic regression is a very popular and attractive machine learning classifier for many good reasons:

It shares similar properties to linear regression.

It is very fast and efficient.

The coefficients are interpretable (although somewhat complex). They represent the change in log odds due to the input variables.

It can also perform well on a small number of observations.

Generally, logistic regression is considered at the lower end when compared with other competitive supervised machine learning algorithms.

## Probability and odds

Probability describes the likeliness of some event to happen or occur on a numerical scale between 0 (impossible) and 1 (sure). The higher the probability is, the more likely the event will occur.

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