Data Science Projects with Python

Chapter 4:

The Bias-Variance Trade-off

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

This chapter presents the final details of logistic regression and equips you with the tools for improving underfitting and overfitting by employing ...