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):

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
- 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

## Usage

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

## 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.