In this chapter, we looked at using probabilistic linear models to predict a qualitative response with two generalized linear model methods: logistic regression, and multivariate adaptive regression splines. We explored using the weight of information and information value as a technique to do univariate feature selection. We covered the concept of finding the proper probability threshold to minimize classification error. Additionally, we began the process of using various performance metrics such as AUC, log-loss, and ROC charts to explore model selection visually and statistically. These metrics proved to be more informative than just pure accuracy, especially in a situation where class labels are highly imbalanced. In the next chapter, we'll cover regularization methods for feature selection, and how it can be used in training your algorithms. We'll see how...
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Table Of Contents
Mastering Machine Learning with R - Third Edition
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Mastering Machine Learning with R
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Overview of this book
Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models.
This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood.
By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work.
Table of Contents (16 chapters)
Preface
Free Chapter
Preparing and Understanding Data
Linear Regression
Logistic Regression
Advanced Feature Selection in Linear Models
K-Nearest Neighbors and Support Vector Machines
Tree-Based Classification
Neural Networks and Deep Learning
Creating Ensembles and Multiclass Methods
Cluster Analysis
Principal Component Analysis
Association Analysis
Time Series and Causality
Text Mining
Creating a Package
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