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

Encoding mixed data

Imagine that you are working for an EdTech company and your job is to predict student grades to target services aimed at bridging the tech skills gap. Your first step is to load data that contains student grades into pandas.

Loading data

The Student Performance dataset, provided by your company, may be accessed by loading the student-por.csv file that has been imported for you.

Start by importing pandas and silencing warnings. Then, download the dataset and view the first five rows:

import pandas as pd
import warnings
df = pd.read_csv('student-por.csv')

Here is the expected output:

Figure 10.1 – The Student Performance dataset as is

Figure 10.1 – The Student Performance dataset as is

Welcome to the world of industry, where data does not always appear as expected.

A recommended option is to view the CSV file. This can be done in Jupyter Notebooks by locating the folder for this chapter and clicking...