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Statistical Application Development with R and Python

You're reading from   Statistical Application Development with R and Python Develop applications using data processing, statistical models, and CART

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Product type Paperback
Published in Aug 2017
Publisher
ISBN-13 9781788621199
Length 432 pages
Edition 2nd Edition
Languages
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Author (1):
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Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Data Characteristics FREE CHAPTER 2. Import/Export Data 3. Data Visualization 4. Exploratory Analysis 5. Statistical Inference 6. Linear Regression Analysis 7. Logistic Regression Model 8. Regression Models with Regularization 9. Classification and Regression Trees 10. CART and Beyond Index

Packages and settings – R and Python

As the chapter reviews some of the techniques in the latter half of the book, we need a lot of packages and functions:

  1. First set the working directory:
    setwd("MyPath/R/Chapter_10")
  2. Load the required R package:
    library(RSADBE)
    library(rpart)
    library(rattle)
    library(MASS)
    library(ROCR)
  3. A host of functions from numpy, pandas, matplotlib, and sklearn will be required for Python analyses:
    Packages and settings – R and Python

Understanding recursive partitions

The name of the library package rpart, shipped along with R, stands for Recursive Partitioning. The package was first created by Terry M Therneau and Beth Atkinson, and is currently maintained by Brian Ripley. We will first have a look at what recursive partitions means.

A complex and contrived relationship is generally not identifiable by linear models. In the previous chapter, we saw the extensions of the linear models in piecewise, polynomial, and spline regression models.

It is also well known that if the order of a model is...

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