Top Packages in R – Machine Learning

Machine Learning is a branch of Artificial Intelligence. It provides the systems to learn and improve without being explicitly programmed. There are a lot of packages for machine learning contributed by the community to R repository for performing predictive modeling easily.

This is the 4th part of our Top Packages in R Series.

If you haven’t seen these part you can read them her

1. Top Packages in R – Data Visualization.
2. Top Packages in R – Data Wrangling(Manipulation).
3. Top Packages in R – Data Import and Export.

For this post we are going to talk about top packages in R for Machine Learning.  

caret

It stands for Classification And Regression Training. Contains miscellaneous functions for training, testing, plotting classification and regression models. It streamlines the process for creating predictive models.

The package contains tools for:

• data splitting
• pre-processing
• feature selection
• model tuning using resampling
• variable importance estimation

randomForest

It provides functions Classification and regression based on a forest of trees using random inputs. It is based on Random Forests by Leo Breiman.

The primary function in randomForest package is randomForest(formula, data).

rpart

Recursive partitioning for classification, regression and survival trees. Its implementation is based on the 1984 book by Breiman, Friedman, Olshen and Stone.

The primary function in rpart package is rpart(formula, data).

e1071

The library includes functions for support vector machines, latent class analysis, fuzzy clustering, shortest path computation, short time Fourier transform, bagged clustering, naive Bayes classifier. Provides function for Support Vector Machine.

glmnet

It provides regularized regression models. It provides an exceptionally efficient functions for fitting the entire lasso or elastic-net regularization path for linear regression. Also includes logistic and multinomial regression models, Poisson regression and the Cox model.

nnet

Provides function for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

h20

The h2o package is for scalable machine learning. It provides R interface for H2O Machine Learning Platform.

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Conclusion

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