Ensemble Machine Learning Cookbook: Over 35 practical recipes to explore ensemble machine learning techniques using Python
Authors: Dipayan Sarkar – Vijayalakshmi Natarajan
ISBN-10: 1789136601
ISBN-13: 9781789136609
Released: 2019-01-31
Print Length 页数: 336 pages
Book Description
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.
Contents
1: GET CLOSER TO YOUR DATA
2: GETTING STARTED WITH ENSEMBLE MACHINE LEARNING
3: RESAMPLING METHODS
4: STATISTICAL AND MACHINE LEARNING ALGORITHMS
5: BAG THE MODELS WITH BAGGING
6: WHEN IN DOUBT,USE RANDOM FORESTS
7: BOOSTING MODEL PERFORMANCE WITH BOOSTING
8: BLEND IT WITH STACKING
9: HOMOGENEOUS ENSEMBLES USING KERAS
10: HETEROGENEOUS ENSEMBLE CLASSIFIERS USING H2O
11: HETEROGENEOUS ENSEMBLE FOR TEXT CLASSIFICATION USING NLP
12: HOMOGENOUS ENSEMBLE FOR MULTICLASS CLASSIFICATION USING KERAS
What You Will Learn
Understand how to use machine learning algorithms for regression and classification problems
Implement ensemble techniques such as averaging,weighted averaging,and max-voting
Get to grips with advanced ensemble methods,such as bootstrapping,bagging,and stacking
Use Random Forest for tasks such as classification and regression
Implement an ensemble of homogeneous and heterogeneous machine learning algorithms
Learn and implement various boosting techniques,such as AdaBoost,Gradient Boosting Machine,and XGBoost
Authors
Dipayan Sarkar
Dipayan Sarkar holds a Masters in Economics and comes with 15+ years of experience. He has also pursued his business analytics studies from Great Lakes Institute of Management. Dipayan has won international challenges in predictive modeling and takes keen interests in the mathematics behind Machine Learning techniques. Before opting to be an independent consultant and mentor in the data science and machine learning space with various organizations,universities & educational institutions,he has served in the capacity of senior data scientist with Fortune 500 companies.
Vijayalakshmi Natarajan
Vijayalakshmi Natarajan holds an ME in Computer Science,comes with 4 years of industry experience. She is a data science enthusiast and is a passionate trainer in the field of data science & data visualization. She takes keen interests in deep diving into Machine Learning techniques. Her specialization includes machine learning techniques in the field of image processing.
