Caret Package – A Practical Guide to Machine Learning in R

Tired of remembering too many different Packages?

One of the biggest challenge beginners in Data Science face is which algorithms to learn and focus on. In case of R, the problem gets accentuated by the fact that one functionality can be achieved by various approaches by using different libraries available in R, which is great but quite frustrating since each package was designed independently and has very different syntax, inputs and outputs. This could be too much for a beginner.

Here is a tip to handle everything from Exploring Data to performing complex Machine learning Algorithms to tuning those algorithms using hyper parameters, everything under a single roof.

All this has been made possible by the years of effort that have gone behind CARET ( Classification And REgression Training) which is possibly the biggest project in R. This package alone is all you need to know for solve almost any supervised machine learning problem. Not only does caret allow you to run a plethora of ML methods, it also provides tools for auxiliary techniques such as:

• Data preparation (imputation, centering/scaling data, removing correlated predictors, reducing skewness) • Data splitting • Variable selection • Model evaluation

Here is an end to end guide to showcase the power of a package that has it all.

We’ll get started by loading the Caret Library and Loan Default dataset in R available in my Working Directory.

# Installing the Library.
library(caret)
## Loading required package: lattice
## Loading required package: ggplot2
# Setting up the working Directory

setwd("D:/Great Learning/Finance and Risk Analytics")

# Reading the dataset.

dataset <- read.csv("raw-data.csv")

Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.