![]() The caret R package provides tools to automatically report on the relevance and. The focus of the presentation will be using caret to implement some of the most common tasks of the data science project lifecycle and to illustrate incorporating it into your daily work. you have learned the most commonly used data normalization techniques using the powerful caret package in R. R packages contain code, data, and documentation in a standardised. ![]() Returning to the above list, we will see that a number of these tasks are directly addressed in the caret package. In this presentation, Dave Langer will provide an introduction to this package. Thankfully, the R community has essentially provided a silver bullet for these issues, the caret package. install.packages ('caret') Creating a simple model We’re gonna do that by using the train () function. If you’re using RStudio (which is recommended), you can also install it by clicking on tools > Install Packages in the toolbar. Not surprisingly, the caret is a sure-fire way to accelerate your velocity as a data scientist. Installing caret is just as simple as installing any other package in R. Most important of all, it provides a common interface for training, tuning, and evaluating more than 200 machine learning algorithms. The package provides capabilities that are ubiquitous in all stages of the data science project lifecycle. If you are a data scientist working with R, the caret package (short for lassification nd gression raining) is a must-have tool in your toolbelt. Caret package is created and maintained by Max Kuhn from Pfizer. Caret package is all you to know for solving any supervised machine learning problem. CARET (Classification And Regression Training) is one of the biggest projects in R. This popularity is due, in part, to R’s huge collection of open-source machine learning algorithms. The R platform has proved to be one of the most powerful for statistical computing and applied machine learning. The R programming language is experiencing a rapid increase in popularity and wide adoption across industries.
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