The idea is to provide a standard interface to users who use both R and Python for building machine learning models. This package provides a scikit-learn's fit, predict interface to train machine learning models in R.
The goal of SuperML is to provide sckit-learn's
transform standard way of building machine learning models in R. It is build on top of latest r-packages which provides optimized way of training machine learning models.
You can install latest stable cran version using (recommended):
You can install superml from github with:
In superml, every machine learning algorithm is called as a
trainer. Following is the list of trainers available as of today:
In addition, there are other useful functions to support modeling tasks such as:
Any machine learning model can be trained using the following steps:
data(iris)library(superml)# random forestrf <- RFTrainer$new(n_estimators = 100)rf$fit(iris, "Species")pred <- rf$predict(iris)
The documentation can be found here: SuperML Documentation
SuperML is my ambitious effort to help people train machine learning models in R as easily as they do in python. I encourage you to use this library, post bugs and feature suggestions in the issues above.
NEWS.mdfile to track changes to the package.