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Mastering Machine Learning with R, Second Edition

You're reading from   Mastering Machine Learning with R, Second Edition Advanced prediction, algorithms, and learning methods with R 3.x

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd 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 (17) 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 and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

Using R

With all systems ready to launch, let's start our first commands. R will take both the strings in 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 10 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...

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