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)

Bagging regressors

Bagging regressors are similar to bagging classifiers. They train each regressor model on a random subset of the original training set and aggregate the predictions. Then, the aggregation averages over the iterations because the target variable is numeric. In the following recipe, we are going to showcase the implementation of a bagging regressor with bootstrap samples.

Getting ready

We will import the required libraries, BaggingRegressor and DecisionTreeRegressor, from sklearn.ensemble and sklearn.tree respectively:

from sklearn.ensemble import BaggingRegressor
from sklearn.tree import DecisionTreeRegressor

We read our dataset, which is bostonhousing.csv, and look at the dimensions of the DataFrame: