R is among the commonest open source machine learning languages. Some other top ones are Python, Matlab, C++, JavaScript, Java, C#, Julia, Shell, TypeScript, and Scala.
an interesting thing is that your choice of language depends on preference and system requirements.
The following are what you can use R programming language for:
R Machine Learning Packages are known as collection of pre-written codes that are reuseable. They are identified as black boxes in R.
In this article, we have come with 21 interesting and top R machine learning packages for you.
This package can be used to produce multiple numbers of decision trees. It is one of the commonest R machine learning packages. randomForest(formula=, data=) is the syntax of this function.
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Recursive Partitioning and Regression Training are what come together to form the word Rpart. Binary tree is displayed in the output and plot () function is what is used in plotting the result of output. Although, prp () function is more preferable to plot () function because of its flexibility and capability.
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Carat is fully known as classification and regression training. It is one of the best for both machine learning and data science. The grid search method of this package can be used to calculate the overall performance of a given model by justing integrating several functions.
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This package is commonly known as one of the best for machine learning.
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This is one of the most common R machine learning packages. One good thing about this package is the fact it’s very easy to implement but comprises just a layer of nodes.
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This R machine language package can be used for SVM (Support Vector Machine), kernel feature analysis, ranking algorithm, dot product primitives, Gaussian process, etc.
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WordCloud is all about a single image having thousands of words. It is known as the visualization of data text.
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This R machine learning package is known as time mining. Time mining tasks are solved by the framework provided by this package.
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This R machine learning package is refered to as Multivariate Imputation via Chained Sequences. Mice package can be used to impute the missing values of machine learning dataset via multiple techniques.
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mlr is a fantastic R machine learning package. This package allows you to perform many machine learning tasks with just a single package.
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This R machine learning package is extensively used as you can perform many operations on it.
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This R machine learning package is mostly used for data relating to science. It is known as a model-based boosting package.
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This R machine learning package can be used for recursive Partitioning. It is known as a computational toolbox.
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This R machine learning package works in a similar way to mlr. Parsnip can be used to sort out linear regression problems.
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This is one of the most loved R machine learning packages. No one loves slow computation of packages, Ranger does computation in a very fast way.
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Purr is the widely known R machine learning package you can use to run your linear regression model on many different parts of data and it can also compute the evaluation metrics for each model.
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EIX is included in DrWhy.AI universe. The DrWhy.AI family of models are into four classes: Model adapters, Model agnostic explainers, Model specific explainers and Automated exploration. EIX happens to be in Model specific explainers.
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OTcust is another very important R machine learning package. It versatility and uses make it unique.
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The R machine learning package is an interface to the “perspective” API.
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It brings about SelectBoost algorithm to enhance how variable selection methods performed in correlated data sets.
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This R machine learning package is best suitable for learning graphs from data.
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R programming language is very important in statistical methods and graphs to explore data. It has numerous packages you can choose from to build avdanced machine learning projects, some very common R machine learning packages are explained above.