Chapter 4. The Bias-Variance Trade-off
Note
Learning Objectives
By the end of this chapter, you will be able to:
Describe the log-loss cost function of logistic regression.
Implement the gradient descent procedure for estimating model parameters.
Articulate the formal statistical assumptions of the logistic regression model.
Characterize the bias-variance trade-off and use it to improve models.
Formulate lasso and ridge regularization and use them in scikit-learn.
Design a function to choose regularization hyperparameters by cross-validation.
Engineer interaction features to improve an underfit model
Note
This chapter presents the final details of logistic regression and equips you with the tools for improving underfitting and overfitting by employing regularization and simple feature engineering.
