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6 Important Tools of Machine Learning for R Programming

Machine learning accounts to the most crucial procedure in data science. R provides various machine learning symbols to its users. Here is some of the important machine learning tools for R programming.


R part helps in understanding the variance which affects the dependent variables based on independent ones. With the help of rpart one can perform both classifications and regressions.


The Multivariate Imputation via Chained Sequences (MICE) is used to impute the missing values during the assessment of the data sets. The probability of data infested with missing values is rather high. In such cases, MICE prove beneficial which uses multiple techniques to make data sets error free.

3)random Forest

It is most widely used algorithm in machine learning. Random Forest helps in generating decision trees. Decision trees further assists in arriving at the conclusion by generating the common output.


For implementing neutral networks in R, nnet is used. Nnet utilizes Artificial Neutral Networks designed after human nervous system.


Classification and Regression Training (caret) is used to find the optimal parameters by experimentation. It helps in facilitating efficient predictive modeling. Data splitting, Data preprocessing, Model tuning, Feature Selection accounts to some of the major tools under caret.


It provides most important feature support vector machine which allows you to work on data which is otherwise challenging to work upon unless supported by higher dimensions.

Machine Learning with R Programming tools grant us insight as to how much important packages machine learning holds under R. For more information Machine Learning Using R in Delhi, Analytics Square is the leading institute which helps in developing R programming skills.