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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
Languages
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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

Boxplot


A boxplot is a standardized way of displaying the distribution of data based on a five number summary (minimum, first quartile (Q1), median, third quartile (Q3), and maximum). Probably, boxplot is the only chart that encapsulates much information in a beautiful looking representation compared to any other charts. Observe the summary of the age variable by each job type. The five summary statistics, that is, min, first quartile, median, mean, third quartile, and max, are described succinctly by a boxplot.

The 25th and 75th percentiles, in the first and third quartiles, are shown by lower and upper hinges, respectively. The upper whisper, which extends from the hinges to the maximum value, is within an IQR of 1.5 *, from the hinge. This is where the IQR is the inter-quartile range or distance between the two quartiles. This is similar in case of the lower hinge. All the points that are outside the hinges are called outliers:

tapply(df_bank_detail$age, df_bank_detail$job, summary)

The output is as follows:

## $admin.
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   20.00   32.00   38.00   39.29   46.00   75.00 
## 
## $'blue-collar'
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   20.00   33.00   39.00   40.04   47.00   75.00 
## 
## $entrepreneur
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   21.00   35.00   41.00   42.19   49.00   84.00 
## 
## $housemaid
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   22.00   38.00   47.00   46.42   55.00   83.00 
## 
## $management
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   21.00   33.00   38.00   40.45   48.00   81.00 
## 
## $retired
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   24.00   56.00   59.00   61.63   67.00   95.00 
## 
## $'self-employed'
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   22.00   33.00   39.00   40.48   48.00   76.00 
## 
0

In the following boxplot, we are looking at the summary of age with respect to each job type. The size of the box that is set to varwidth = TRUE in geom_boxplot shows the number of observations in the particular job type. The wider the box, the larger the number of observations:

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

Figure 1.12: Boxplot of age 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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