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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