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The Caret package in R language stands for Classification and Regression Training and includes different functions to rationalize the model training process for complex regression and classification. The package also eases the process of tuning and accessing different machine learning models in the R language.
The DataExplorer in R is another machine learning package in the R language used for exploratory data analysis. It comes with three primary objectives such as exploratory data analysis, data reporting and feature engineering. DataExplorer analyzes each variable and envisages them with proper graphical techniques.
Dplyr is a set of functions that are designed for enabling data frame manipulation spontaneously. It is beneficial for the data analysts who use this to transform available datasets into a good format for proper analysis and data visualization.
It is another popular open-source data visualization tool used in statistical programming language R. it is primarily a plotting package that helps in creating a complex plot from the data available in the data frame conveniently. Ggplot2 also offers a dynamic programming interface to stipulate the required variables in the plot, their display features, and general visual properties.
kernlab is a suitable package for Kernel-based machine learning methods used in R. it offers a solid framework for developing and using kernel-based algorithms and features dot product primitives, implementation of support vector machines, ranking algorithm and online kernel methods. Kernlab also offers a general-purpose quadratic programming solving technique.
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R is one of the most preferred statistical analysis languages and instrumental in carrying out descriptive analysis, regression equations and hierarchical linear modelling. It is a nice replacement for the complete MS Office tool. You need not open the lengthy excel sheets or MS word as you can perform all your tasks using R. it is also used to create APA formatted tables, simulate, resample, and run multi-core processors.
To date, R was quite difficult to understand as it was not fully structured. However, with the advancement of a collection of packages, especially tidy verse, data manipulation with R has become intuitive. It is also quite easy to implement the best algorithm for machine learning with R., on the other hand, the packages like TensorFlow offers a great platform to develop high-end machine learning techniques. You can also communicate with other languages using R like Python, Java and C++. So ultimately, R has evolved since its inception and speed up its process; now, you can use the statistical programming language without facing any difficulty.
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