Introduction: XGBoost

Get introduced to our topics for this chapter: scalable end-to-end tree boosting systems called XGBoost and LightGBM.

We'll cover the following...

Overview
Packages for accurate predictive models

Overview

After reading this chapter, you will be able to describe the concept of gradient boosting, the fundamental idea underlying the XGBoost package. You will then train XGBoost models on synthetic data, while learning about early stopping as well as several XGBoost hyperparameters along the way. In addition to using a similar method to grow trees as we have previously (by setting max_depth), you’ll also discover a new way of growing trees that is offered by XGBoost: loss-guided tree growing. After learning about XGBoost, you’ll then be introduced to a new and powerful way of explaining model predictions, called SHAP ...

Introduction

Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Introduction to scikit-learn and Model Evaluation

Fake News Detection Using Scikit-learn

(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

Predict Frog Toxicity with Python and XGBoost

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

(Challenge) Deriving Financial Insights

Appendix

Introduction: XGBoost

Overview