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README

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.

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:

• 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

To compute text similarity, following functions are available:

• bm_25: Computes bm25 distance
• dot: Computes dot product between two vectors
• dotmat: Computes dot product between vector & matrix

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

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.

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