If you’ve been using R for a while, and you’ve been working with basic data visualization and data exploration techniques, the next logical step is to start learning some machine learning.
To help you begin learning about machine learning in R, I’m going to introduce you to an R package: the caret package. We’ll build a very simple machine learning model as a way to learn some of caret’s basic syntax and functionality.
But before diving into caret, let’s quickly discuss what machine learning is and why we use it.
What is machine learning?
Machine learning is the study of data-driven, computational methods for making inferences and predictions.
Without going into extreme depth here, let’s unpack that by looking at an example.
A simple example
Imagine that you want to understand the relationship between car weight and car fuel efficiency (I.e., miles per gallon); how is fuel efficiency effected by a car’s weight?
To answer this question, you could obtain a dataset with several different car models, and attempt to identify a relationship between weight (which we’ll call wt) and miles per gallon (which we’ll call mpg).
A good starting point would be to simply plot the data, so first, we’ll create a scatterplot using R’s ggplot:
require(ggplot2) ggplot(data = mtcars, aes(x = wt, y = mpg)) + geom_point()
Just examining the data visually, it’s pretty clear that there’s some relationship. But if we want to make more precise claims about the relationship between wt and mpg, we need to specify this relationship mathematically. So, as we press forward in our analysis, we’ll make the assumption that this relationship can be described mathematically; more precisely, we’ll assume that this relationship can be described by some mathematical function, 
. In the case of the above example, we’ll be making …read more
Source:: r-bloggers.com