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)
1
Section 1: Bagging and Boosting
6
Section 2: XGBoost
10
Section 3: Advanced XGBoost

Engineering new columns

"Almost always I can find open source code for what I want to do, and my time is much better spent doing research and feature engineering."

Owen Zhang, Kaggle Winner

(https://medium.com/kaggle-blog/profiling-top-kagglers-owen-zhang-currently-1-in-the-world-805b941dbb13)

Many Kagglers and data scientists have confessed to spending considerable time on research and feature engineering. In this section, we will use pandas to engineer new columns of data.

What is feature engineering?

Machine learning models are as good as the data that they train on. When data is insufficient, building a robust machine learning model is impossible.

A more revealing question is whether the data can be improved. When new data is extracted from other columns, these new columns of data are said to be engineered.

Feature engineering is the process of developing new columns of data from the original columns. The question is not whether you should...