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 a random forest for predicting credit card defaults using scikit-learn

The scikit-learn library implements random forests by providing two estimators: RandomForestClassifier and RandomForestRegressor. They take various parameters, some of which are explained as follows:

  • n_estimators: This parameter is the number of trees the algorithm builds before taking a maximum vote or the average prediction. In general, the higher the number of trees the better the performance and the accuracy of the predictions, but it also costs more in terms of computation.
  • max_features: This parameter is the maximum number of features that the random forest is allowed to try in an individual tree.
  • min_sample_leaf: This parameter determines the minimum number of leaves that are required to split an internal node.
  • n_jobs: This hyperparameter tells the engine how many jobs to run in parallel...