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

Customizing scikit-learn transformers

Now that we have a process for transforming the DataFrame into a machine learning-ready sparse matrix, it would be advantageous to generalize the process with transformers so that it can easily be repeated for new data coming in.

Scikit-learn transformers work with machine learning algorithms by using a fit method, which finds model parameters, and a transform method, which applies these parameters to data. These methods may be combined into a single fit_transform method that fits and transforms data in one line of code.

When used together, various transformers, including machine learning algorithms, may work together in the same pipeline for ease of use. Data is then placed in the pipeline that is fit and transformed to achieve the desired output.

Scikit-learn comes with many great transformers, such as StandardScaler and Normalizer to standardize and normalize data, respectively, and SimpleImputer to convert null values. You have to...