Heads up: It might take some time: > install.packages("caret", dependencies = c("Depends", "Suggests"))
#CARET IN R INSTALL#
To install Caret on your system, use the following command. But it does assume that you already have all the algorithms installed on your system. Instead, it loads them only when the packages are needed. Additionally, it provides several features which makes it a one stop solution for all the modeling needs for supervised machine learning problems.Ĭaret tries not to load all the packages it depends upon at the start. To put in simple words, Caret is essentially a wrapper for 200+ machine learning algorithms. Variable importance estimation using Caret.While caret definitely simplifies the job to a degree, it can not take away the hard work and practice you need to put in to become a master at machine learning. Today, we’ll work on the Loan Prediction problem-III to show you the power of Caret package. To get an in-depth overview of various functionalities provided by Caret, you can refer to this article. It provides a uniform interface to several machine learning algorithms and standardizes various other tasks such as Data splitting, Pre-processing, Feature selection, Variable importance estimation, etc. This package alone is all you need to know for solve almost any supervised machine learning problem. remember the different package names for each algorithm.Īll 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.So, then how do you transform from a beginner to a data scientist building hundreds of models and stacking them together? There certainly isn’t any shortcut but what I’ll tell you today will make you capable of applying hundreds of machine learning models without having to: In case of R, the problem gets accentuated by the fact that various algorithms would have different syntax, different parameters to tune and different requirements on the data format. One of the biggest challenge beginners in machine learning face is which algorithms to learn and focus on.
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