Ensemble Machine Learning Cookbook

Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more

Key FeaturesApply popular machine learning algorithms using a recipe-based approachImplement boosting, bagging, and stacking ensemble methods to improve machine learning modelsDiscover real-world ensemble applications and encounter complex challenges in Kaggle competitionsBook DescriptionEnsemble 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.

What you will learnUnderstand how to use machine learning algorithms for regression and classification problemsImplement ensemble techniques such as averaging, weighted averaging, and max-votingGet to grips with advanced ensemble methods, such as bootstrapping, bagging, and stackingUse Random Forest for tasks such as classification and regressionImplement an ensemble of homogeneous and heterogeneous machine learning algorithmsLearn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoostWho this book is forThis book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. Working knowledge of Python programming and basic statistics is a must to help you grasp the concepts in the book.

Table of ContentsGet Closer to Your Data with Exploratory Data AnalysisGetting Started with Ensemble Machine LearningResampling MethodsStatistical & Machine Learning AlgorithmsBag the Models with BaggingWhen in Doubt, use Random ForestBoost up Model Performance with BoostingBlend it with StackingHomogeneous Ensemble for Hand-Written Digits RecognitionHeterogeneous Ensemble Classifiers for Credit Card Default PredictionHeterogeneous Ensemble for Sentiment Analysis using NLPHeterogeneous Ensemb

Size: A4(20*28cm)

Printing: 80 gm – color

Cover: Softcover