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Mastering Machine Learning with R
Mastering Machine Learning with R

Mastering Machine Learning with R: Advanced machine learning techniques for building smart applications with R 3.5 , Third Edition

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Mastering Machine Learning with R

Linear Regression

"An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem."
– John Tukey

It's essential that we get started with a simple yet extremely effective technique that's been used for a long time: linear regression. Albert Einstein is believed to have remarked at one time or another that things should be made as simple as possible, but no simpler. This is sage advice and a good rule of thumb in the development of algorithms for machine learning. Considering the other techniques that we'll discuss later, there's no simpler model than tried and tested linear regression, which uses the least squares approach to predict a quantitative outcome. We can consider it to be the foundation of all the methods that we'll discuss later, many of which are mere extensions. If you can...

Univariate linear regression

We begin by looking at a simple way to predict a quantitative response, Y, with one predictor variable, x, assuming that Y has a linear relationship with x. The model for this can be written as follows:

We can state it as the expected value of Y is a function of the parameters (the intercept) plus (the slope) times x, plus an error term e. The least squares approach chooses the model parameters that minimize the Residual Sum of Squares (RSS) of the predicted y values versus the actual Y values. For a simple example, let's say we have the actual values of Y1 and Y2 equal to 10 and 20 respectively, along with the predictions of y1 and y2 as 12 and 18. To calculate RSS, we add the squared differences:

This, with simple substitution, yields the following:

Before we begin with an application, I want to point out that if you read the headlines...

Multivariate linear regression

In the case study that follows, we're going to look at the application of some exciting methods on an interesting dataset. Like in the previous chapter, once the data is loaded we'll treat it, but unlike the previous example, we'll split it into training and testing sets. Given the dimensionality of the data, feature reduction and selection are critical.

We'll explore the oft-maligned stepwise selection, then move on to one of my favorite methodologies, which is Multivariate Adaptive Regression Splines (MARS). If you're not using MARS, I highly recommend it. I've been told, but cannot verify it, that Max Kuhn stated in a conference that it's his starting procedure. I'm not surprised if it's true. I learned the technique from a former Senior Director of Analytics at one of the largest banks in the world...

Summary

In the context of machine learning, we train a model and test it to predict an outcome. In this chapter, we had an in-depth look at the simple yet extremely effective methods of linear regression and MARS to predict a quantitative response. We also applied the data preparation paradigm put forth in Chapter 1, Preparing and Understanding Data, to quickly and efficiently get the data ready for modeling. We produced several simple plots to understand the response we were trying to predict, explore model assumptions, and model results.

Later chapters will cover more advanced techniques like Logistic regression, Support Vector Machines, Classification, Neural Networks, and Deep Learning but many of them are mere extensions of what we've learned in this chapter.

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Key benefits

  • Build independent machine learning (ML) systems leveraging the best features of R 3.5
  • Understand and apply different machine learning techniques using real-world examples
  • Use methods such as multi-class classification, regression, and clustering

Description

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.

Who is this book for?

This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.

What you will learn

  • Prepare data for machine learning methods with ease
  • Understand how to write production-ready code and package it for use
  • Produce simple and effective data visualizations for improved insights
  • Master advanced methods, such as Boosted Trees and deep neural networks
  • Use natural language processing to extract insights in relation to text
  • Implement tree-based classifiers, including Random Forest and Boosted Tree

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 31, 2019
Length: 354 pages
Edition : 3rd
Language : English
ISBN-13 : 9781789618006
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Product Details

Publication date : Jan 31, 2019
Length: 354 pages
Edition : 3rd
Language : English
ISBN-13 : 9781789618006
Category :
Languages :

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Table of Contents

15 Chapters
Preparing and Understanding Data Chevron down icon Chevron up icon
Linear Regression Chevron down icon Chevron up icon
Logistic Regression Chevron down icon Chevron up icon
Advanced Feature Selection in Linear Models Chevron down icon Chevron up icon
K-Nearest Neighbors and Support Vector Machines Chevron down icon Chevron up icon
Tree-Based Classification Chevron down icon Chevron up icon
Neural Networks and Deep Learning Chevron down icon Chevron up icon
Creating Ensembles and Multiclass Methods Chevron down icon Chevron up icon
Cluster Analysis Chevron down icon Chevron up icon
Principal Component Analysis Chevron down icon Chevron up icon
Association Analysis Chevron down icon Chevron up icon
Time Series and Causality Chevron down icon Chevron up icon
Text Mining Chevron down icon Chevron up icon
Creating a Package Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
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(3 Ratings)
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4 star 0%
3 star 0%
2 star 33.3%
1 star 66.7%
naima Jul 13, 2021
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This book has the worst codes I have ever seen.
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floren25 Nov 24, 2020
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Este libro está escrito con la nueva forma de programar en R basada en tidyverse. Tengo cierta familiaridad con esta forma de escribir código desde que leí «R for Data Science», de Hadley Wickham y Garrett Grolemund, en que se expone en detalle todo el asunto.El problema con la lectura de este texto es que el código suministrado por el autor en la página web que sirve de soporte a este libro está plagado de errores. Y, lo que es más enigmático para mí, allí donde el código funciona, da como resultado al ejecutarlo en R algo distinto de lo que aparece en el texto escrito. Otro detalle adicional es que, a pesar de que la tercera edición de este libro es de 2019, Lesmeister emplea paquetes de R que han caído en desuso y están obsoletos. Todo esto ha hecho que la lectura haya sido bastante frustrante. Una pena porque me las prometía felices con este libro.
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Dr. Marilou Haines May 31, 2020
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This is a very unfriendly website. First you need to register to get access to the data. Then, you need to look forever to find the support tab. And once you find it, you get stuck because there is no way to finalize the step. I have a book, but no files to work with. I may just have to send the book back.
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