guhjy / superml

Build machine learning models in R like using python's scikit-learn library

superml

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 superml from github with:

# install.packages("devtools")
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)
• RFTrainer: Random Forest Model
• KNNTrainer: K-Nearest Neighbour Model
• KMeansTrainer: KMeans 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
• GridSearch: For hyperparameter optimization
• RandomSearch: 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)
rf <- RFTrainer$new(n_estimators = 100)
rf$fit(iris, "Species")
pred <- rf$predict(iris)