README.Rmd

---
output: github_document
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# 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):

```{r, eval=FALSE}
install.packages("superml")
```

You can install latest development version from github with:

```{r gh-installation, eval = FALSE}
# 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:<br/>

 * 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:

```{r example}
data(iris)
library(superml)
rf <- RFTrainer$new(n_estimators = 100)
rf$fit(iris, "Species")
pred <- rf$predict(iris)

```