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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Advanced machine learning techniques for building smart applications with R 3.5

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Product type Paperback
Published in Jan 2019
Publisher
ISBN-13 9781789618006
Length 354 pages
Edition 3rd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (16) Chapters Close

Preface 1. Preparing and Understanding Data 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Advanced Feature Selection in Linear Models 5. K-Nearest Neighbors and Support Vector Machines 6. Tree-Based Classification 7. Neural Networks and Deep Learning 8. Creating Ensembles and Multiclass Methods 9. Cluster Analysis 10. Principal Component Analysis 11. Association Analysis 12. Time Series and Causality 13. Text Mining 14. Creating a Package 15. Other Books You May Enjoy

Data understanding

The dataset for analysis here is DNA pulled from mlbench. You don't have to install the package as I've put it in a CSV file and placed it on GitHub: https://github.com/datameister66/MMLR3rd/blob/master/dna.csv.

Install the packages as needed and load the data:

> library(magrittr)

> install.packages("earth")

> install.packages("glmnet")

> install.packages("mlr")

> install.packages("randomForest")

> install.packages("tidyverse")

dna <- read.csv("dna.csv")

The data consists of 3,181 observations, 180 input features coded as binary indicators, and the Class response. The response is a factor with three labels indicating a DNA type either ei, ie, or neither—coded as n. The following is a table of the target labels:

> table(dna$Class)

ei ie n
767 765 1654

This data...

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