XGBoost’s approach to handling missing data

As a final note on the use of both XGBoost and SHAP, one valuable trait of both packages is their ability to handle missing values. Recall that in the chapter “Data Exploration and Cleaning,” we found that some samples in the case study data had missing values for the PAY_1 feature. So far, our approach has been to simply remove these samples from the dataset when building models. This is because, without specifically addressing the missing values in some way, the machine learning models implemented by scikit-learn cannot work with the ...

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

Missing Data

XGBoost’s approach to handling missing data