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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Master machine learning techniques with R to deliver insights for complex projects

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783984527
Length 400 pages
Edition 1st Edition
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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 (15) Chapters Close

Preface 1. A Process for Success 2. Linear Regression – The Blocking and Tackling of Machine Learning FREE CHAPTER 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis and Recommendation Engines 11. Time Series and Causality 12. Text Mining A. R Fundamentals Index

Using R

With all the systems ready to launch, let's start our first commands. R will take both the strings in the quotes or simple numbers. Here, we will put one command as a string and one command as a number. The output is the same as the input:

> "Let's Go Sioux!"
[1] "Let's Go Sioux!"

> 15
[1] 15

R can also act as a calculator:

> ((22+5)/9)*2
[1] 6

Where R starts to shine is in the creation of vectors. Here, we will put the first ten numbers of the Fibonacci sequence in a vector using the c() function, which stands for combining the values to a vector or list (concatenate):

> c(0,1,1,2,3,5,8,13,21,34) #Fibonacci sequence
 [1]  0  1  1  2  3  5  8 13 21 34

Note that in this syntax, I included a comment, Fibonacci sequence. In R, anything after the # key on the command line is not executed.

Now, let's create an object that contains these numbers of the sequence. You can assign any vector or list to an object. In most of the R code, you...

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