Summary

In this chapter, we introduced the final details of logistic regression and continued to use scikit-learn to fit logistic regression models. We gained more visibility of how the model fitting process works by learning about the concept of a cost function, which is minimized by the gradient descent procedure to estimate model parameters during model fitting.

We also learned of the need for regularization, by introducing the concepts of underfitting and overfitting. In order to reduce overfitting, we saw how to adjust the cost function to regularize the coefficients of a logistic regression model using an L1 or L2 penalty. We used cross-validation to select the amount of regularization, by tuning the regularization hyperparameter. To reduce underfitting, we gained experience with a simple feature engineering technique by creating interaction features for the case study data.

We are now familiar with some of the most important concepts in machine learning. We have, so far, only used a...