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

Analyzing the confusion matrix

A confusion matrix is a table that summarizes the correct and incorrect predictions of a classification model. The confusion matrix is ideal for analyzing imbalanced data because it provides more information on which predictions are correct, and which predictions are wrong.

For the Exoplanet subset, here is the expected output for a perfect confusion matrix:

array([[88, 0],
       [ 0,  12]])

When all positive entries are on the left diagonal, the model has 100% accuracy. A perfect confusion matrix here predicts 88 non-exoplanet stars and 12 exoplanet stars. Notice that the confusion matrix does not provide labels, but in this case, labels may be inferred based on the size.

Before getting into further detail, let's see the actual confusion matrix using scikit-learn.


Import confusion_matrix from sklearn.metrics as follows:

from sklearn.metrics import confusion_matrix