Book Image

Hands-On Gradient Boosting with XGBoost and scikit-learn

By : Corey Wade
Book Image

Hands-On Gradient Boosting with XGBoost and scikit-learn

By: Corey Wade

Overview of this book

XGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently. The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You’ll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You’ll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. Details in XGBoost are explored with a focus on speed enhancements and deriving parameters mathematically. With the help of detailed case studies, you’ll practice building and fine-tuning XGBoost classifiers and regressors using scikit-learn and the original Python API. You'll leverage XGBoost hyperparameters to improve scores, correct missing values, scale imbalanced datasets, and fine-tune alternative base learners. Finally, you'll apply advanced XGBoost techniques like building non-correlated ensembles, stacking models, and preparing models for industry deployment using sparse matrices, customized transformers, and pipelines. By the end of the book, you’ll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.
Table of Contents (15 chapters)
Section 1: Bagging and Boosting
Section 2: XGBoost
Section 3: Advanced XGBoost

Combining hyperparameters

It's time to combine all the components of this chapter to improve upon the 78% score obtained through cross-validation.

As you know, there is no one-size-fits-all approach to hyperparameter fine-tuning. One approach is to input all hyperparameter ranges with RandomizedSearchCV. A more systematic approach is to tackle hyperparameters one at a time, using the best results for subsequent iterations. All approaches have advantages and limitations. Regardless of strategy, it's essential to try multiple variations and make adjustments when the data comes in.

One hyperparameter at a time

Using a systematic approach, we add one hyperparameter at a time, aggregating results along the way.


Even though the n_estimators value of 2 gave the best result, it's worth trying a range on the grid_search function, which uses cross-validation:

grid_search(params={'n_estimators':[2, 25, 50, 75, 100]})

The output is as...