Summary
Explore the concepts of logistic regression for binary classification. Understand the relationship between probability, odds, and log odds, learn to implement logistic regression models, interpret coefficients, and evaluate models using accuracy and confusion matrices to gain insights into their performance.
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). ...