# AI News, A quick introduction to machine learning in R with caret

- On Sunday, June 3, 2018
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## A quick introduction to machine learning in R with caret

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.

We’ll build a very simple machine learning model as a way to learn some of caret’s basic syntax and functionality.

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).

Said differently, machine learning provides a set of computational methods that accept data observations as inputs, and subsequently estimate that mathematical function, ;

Therefore, you wouldn’t need to understand the mathematics, physics, and electrical engineering principles that would be required to construct those tools from scratch.

To be clear, you’d still need to learn how to use those tools, but you wouldn’t need a deep understanding of math and electrical engineering to operate them.

When you’re first getting started with machine learning, the situation is very similar: you can learn to use some of the tools, without knowing the deep mathematics that makes those tools work.

has many packages for implementing various machine learning methods, but unfortunately many of these tools were designed separately, and they are not always consistent in how they work.

As the name implies, the caret package gives you a toolkit for building classification models and regression models.

To simplify the process, caret provides tools for almost every part of the model building process, and moreover, provides a common interface to these different machine learning methods.

When using caret, different learning methods like linear regression, neural networks, and support vector machines, all share a common syntax (the syntax is basically identical, except for a few minor changes).

Moreover, additional parts of the machine learning workflow – like cross validation and parameter tuning – are built directly into this common interface.

To say that more simply, caret provides you with an easy-to-use toolkit for building many different model types and executing critical parts of the ML workflow.

Now that you’ve been introduced to caret, let’s return to the example above (of mpg vs wt) and see how caret works.

As noted above, in mathematical terms, this means identifying a function, , that describes the relationship between wt and mpg.

Here in this example, we’re going to make an additional assumption that will simplify the process somewhat: we’re going to assume that the relationship is linear;

In terms of our modeling effort, this means that we’ll be using linear regression to build our machine learning model.

Without going into the details of linear regression (I’ll save that for another blog post), let’s look at how we implement linear regression with caret.

Now that we have a simple model, let’s quickly extract the regression coefficients and plot the model (i.e., plot the linear function that describes the relationship between mpg and wt).

The machine learning method you want to use (in this case “linear regression”) In caret’s syntax, you identify the target variable and input variables using the “formula notation.”

If we translate this line of code into English, we’re effectively telling train(), “build a model that predicts mpg (miles per gallon) on the basis of wt (car weight).”

Said differently, if we’re using the formula mpg ~ wt to indicate the target and predictor variables, then we’re using the data = parameter to tell caret where to find those variables.

So basically, data = mtcars tells the caret function that the data and the relevant variables can be found in the mtcars dataset.

Although it’s beyond the scope of this blog post to discuss all of the possible learning methods that we could use here, there are many different methods we could use.

This is a good place to reiterate one of caret’s primary advantages: switching between model types is extremely easy when we use caret’s train() function.

- On Friday, January 18, 2019

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