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Applied Supervised Learning with R

Applied Supervised Learning with R

By : Karthik Ramasubramanian, Jojo Moolayil
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Applied Supervised Learning with R

Applied Supervised Learning with R

By: Karthik Ramasubramanian, Jojo Moolayil

Overview of this book

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.
Table of Contents (12 chapters)
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Applied Supervised Learning with R
Preface

Write R Markdown Files for Code Reproducibility


The considerable success of analytics is a result of the way the information and knowledge network around the subject started to spread. More open source communities emerged, developers were happily sharing their work with the outer world, and many data projects were becoming reproducible. This change meant that work started by one person was soon getting adapted, improvised, and modified in many different forms by a community of people before it got adopted in an entirely different domain than the one from where it initially emerged. Imagine every research work that gets published in conference submitting a collection of code and data that is easily reproducible along with their research paper. This change is accelerating the pace at which an idea meets reality, and innovation will start to boom.

Now, let's see how to create such reproducible work in a single file that we call the R Markdown file. In the following activity, we will demonstrate how to create a new R Markdown file in RStudio. A detailed intro to R Markdown could be found at https://rmarkdown.rstudio.com/lesson-1.html.

In the next activity, you will recreate the code shown in Exercise 4, Reading a CSV File with Text Column and Storing the Data in VCorpus, into an R Markdown. Observe in Figure 4.2 that you have just written the explanation and the code in R Markdown, and when the Knit to Word action is performed, it interweaves the explanation, code, and its output neatly into a word document.

Activity 1: Create an R Markdown File to Read a CSV File and Write a Summary of Data

In this activity, we will create a R Markdown file to read a CSV file and print a small summary of the data in a word file:

Perform the following steps to complete the activity:

  1. Open RStudio and navigate to the R Markdown option:

    Figure 1.4: Creating a new R Markdown file in Rstudio

  2. Provide the Title and Author name for the document and select the Default Output Format as Word:

    Figure 1.5: Using the read.csv method to read the data

  3. Use the read.csv() method to read the bank-full.csv file.

  4. Finally, print the summary into a word file using the summary method.

    The output is as follows:

    Figure 1.6: Final output after using the summary method

    Note

    The solution for this activity can be found at page 438.

CONTINUE READING
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Applied Supervised Learning with R
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