In ssi-ashraf/superml: Build Machine Learning Models Like Using Python's Scikit-Learn Library in R

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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 latest stable cran version using (recommended):

install.packages("superml")

You can install latest development version 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
• 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 (experimental)
• smoothMean: Target encoding with smoothing

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)

ssi-ashraf/superml documentation built on Nov. 5, 2019, 9:18 a.m.