Build Machine Learning Models Like Using Python's Scikit-Learn Library in R

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 fit,predict,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.

Installation

You can install latest stable cran version using (recommended):

install.packages("superml")

You can install superml from github with:

devtools::install_github("saraswatmks/superml")

Description

In superml, every machine learning algorithm is called as a trainer. Following is the list of trainers available as of today:

• LMTrainer: used to train linear, logistic, ridge, lasso models
• KNNTrainer: K-Nearest Neighbour Models
• KMeansTrainer: KMeans Model
• NBTrainer: Naive Baiyes Model
• SVMTrainer: SVM Model
• RFTrainer: Random Forest Model
• XGBTrainer: XGBoost Model

In addition, there are other useful functions to support modeling tasks such as:

• CountVectorizer: Create Bag of Words model
• TfidfVectorizer: Create TF-IDF feature model
• LabelEncoder: Convert categorical features to numeric
• GridSearchCV: For hyperparameter optimization
• RandomSearchCV: For hyperparameter optimization
• kFoldMean: Target encoding
• smoothMean: Target encoding

Usage

Any machine learning model can be trained using the following steps:

data(iris)
library(superml)
 
# random forest
rf <- RFTrainer$new(n_estimators = 100)
rf$fit(iris, "Species")
pred <- rf$predict(iris)

Documentation

The documentation can be found here: SuperML Documentation

Contributions & Support

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.