Book Image

Ensemble Machine Learning Cookbook

By : Dipayan Sarkar, Vijayalakshmi Natarajan
Book Image

Ensemble Machine Learning Cookbook

By: Dipayan Sarkar, Vijayalakshmi Natarajan

Overview of this book

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.
Table of Contents (14 chapters)

Implementing the extreme gradient boosting method for glass identification using XGBoost with scikit-learn

XGBoost stands for extreme gradient boosting. It is a variant of the gradient boosting machine that aims to improve performance and speed. The XGBoost library in Python implements the gradient boosting decision tree algorithm. The name gradient boosting comes from its us of the gradient descent algorithm to minimize loss when adding new models. XGBoost can handle both regression and classification tasks.

XGBoost is the algorithm of choice among those participating in Kaggle competitions because of its performance and speed of execution in difficult machine learning problems.

Some of the important parameters that are used in XGBoost are as follows:

  • n_estimators/ntrees: This specifies the number of trees to build. The default value is 50.
  • max_depth: This specifies the maximum...