If you wish to change your working directory then the setwd() can complete our task. You can get the path of your current working directory by running getwd() command in R console. After downloading the data file, you need to set your working directory via console else save the data file in your current working directory. All the data values are separated by commas. First of all, we need to download the dataset. Frist install the package ot using the command install.packages(“ot”) Data Importįor importing the data and manipulating it, we are going to use data frames. In case if you face any error while running the code. The “ot” package will help to get a visual plot of the decision tree. As we mentioned above, caret helps to perform various tasks for our machine learning work. Decision Tree classifier implementation in R with Caret Package R Library importįor implementing Decision Tree in r, we need to import “caret” package & “ot”. To model a classifier for evaluating the acceptability of car using its given features. The above table shows all the details of data. We will try to build a classifier for predicting the Class attribute. The Cars Evaluation data set consists of 7 attributes, 6 as feature attributes and 1 as the target attribute. Open R console and install it by typing below command: install.packages("caret") It is similar to the sklearn library in python.įor using it, we first need to install it. It holds tools for data splitting, pre-processing, feature selection, tuning and supervised – unsupervised learning algorithms, etc. The R programming machine learning caret package( Classification And REgression Training) holds tons of functions that helps to build predictive models. We just need to call functions for implementing algorithms with the right parameters. The beauty of these packages is that they are well optimized and can handle maximum exceptions to make our job simple. The developer community of R programming language has built the great packages Caret to make our work easier. To work on big datasets, we can directly use some machine learning packages. 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.ĭecision Tree Classifier implementation in R Click To Tweet Why use the Caret Package To get more out of this article, it is recommended to learn about the decision tree algorithm. Now we are going to implement Decision Tree classifier in R using the R machine learning caret package. As we have explained the building blocks of decision tree algorithm in our earlier articles. The decision tree classifier is a supervised learning algorithm which can use for both the classification and regression tasks. Decision Tree Classifier in R Decision Tree Classifier implementation in R
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