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Decision Tree Classifier Implementation In R

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Article originally posted on Data Science Central. Visit Data Science Central

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The decision tree classifier is a supervised learning algorithm which can use for both the classification and regression tasks. As we have explained the building blocks of decision tree algorithm in our earlier articles. Now we are going to implement Decision Tree classifier in R using the R machine learning caret package.

To get more out of this article, it is recommended to learn about the decision tree algorithm. If you don’t have the basic understanding on Decision Tree classifier, it’s good to spend some time on understanding how the decision tree algorithm works.

Why use the Caret Package

To work on big datasets, we can directly use some machine learning packages. The developer community of R programming language has built the great packages Caret to make our work easier. The beauty of these packages is that they are well optimized and can handle maximum exceptions to make our job simple. We just need to call functions for implementing algorithms with the right parameters.

Caret Package Installation

The R programming machine learning caret package( Classification And REgression Training) holds tons of functions that helps to build predictive models. It holds tools for data splitting, pre-processing, feature selection, tuning and supervised – unsupervised learning algorithms, etc. It is similar to the sklearn library in python.

For using it, we first need to install it. Open R console and install it by typing below command:

The installed caret package provides us direct access to various functions for training our model with different machine learning algorithms like Knn, SVM, decision tree, linear regression, etc.
To read more, click here.

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