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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Histogram


A histogram is a visualization consisting of rectangles whose area is proportional to the frequency of a variable and whose width is equal to the class interval.

The height of the bar in a histogram represents the number of observations in each group. In the following example, we are counting the number of observations for each type of job and marital status. y is a binary variable checking whether the client subscribed a term deposit or not (yes, no) as a response to the campaign call.

It looks like blue-collar individuals are responding to the campaign calls the least, and individuals in management jobs are subscribing to the term deposit the most:

ggplot(data = df_bank_detail) +
  geom_bar(mapping = aes(x=job, fill = y)) +
  theme(axis.text.x = element_text(angle=90, vjust=.8, hjust=0.8))

Figure 1.11: Histogram of count and job

You have been reading a chapter from
Applied Supervised Learning with R
Published in: May 2019
Publisher:
ISBN-13: 9781838556334
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