Summary

In this chapter, we delved into the world of logistic regression, its theoretical underpinnings, and its practical applications. We started by exploring the fundamental construct of logistic regression and its comparison with linear regression. We then introduced the concept of the sigmoid transformation, a crucial element in logistic regression, which ensures the output of our model is bounded between 0 and 1. This section helped us better understand the advantages of logistic regression for binary classification tasks.

Next, we delved into the concept of log odds and odds ratio, two critical components of the logistic regression model. Understanding these allowed us to comprehend the real-world implications of the model’s predictions and to interpret its parameters effectively. The chapter then introduced the CEL, the cost function used in logistic regression. Specifically, we discussed how this loss function ensures our model learns to predict accurate probabilities...