Introduction: The Bias-Variance Trade-Off

Get introduced to our topic for this chapter: bias-variance trade-off.

We'll cover the following

Section overview
Understanding gradient descent
Bias-variance trade-off

Section overview

In this chapter, we’ll cover the remaining elements of logistic regression, including what happens when you call fit to train the model, and the statistical assumptions you should be aware of when using this modeling technique. You will learn how to use L1 and L2 regularization with logistic regression to prevent overfitting and how to use the practice of cross-validation to decide the regularization strength. After reading this section, you will be able to use logistic regression in your work and employ regularization in the model fitting process to take advantage of the bias-variance trade-off and improve model performance on unseen data.

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Introduction

Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Introduction to scikit-learn and Model Evaluation

(Challenge) Logistic Regression and Precision-Recall Curve

Details of Logistic Regression and Feature Extraction

(Challenge) Logistic Regression Model and Coefficients

The Bias-Variance Trade-Off

(Challenge) Cross-Validation and Feature Engineering

Decision Trees and Random Forests

(Challenge) Cross-Validation Grid Search with Random Forest

Gradient Boosting, XGBoost, and SHAP Values

(Challenge) XGBoost and SHAP Explanation for Case Study Data

Test Set Analysis, Financial Insights, and Delivery to the Client

(Challenge) Deriving Financial Insights

Appendix

Introduction: The Bias-Variance Trade-Off

Section overview