fastml: Fast Machine Learning Model Training and Evaluation

fastml is a streamlined R package designed to simplify the training, evaluation, and comparison of multiple machine learning models. It offers comprehensive data preprocessing, supports a wide range of algorithms with hyperparameter tuning, and provides performance metrics alongside visualization tools to facilitate efficient and effective machine learning workflows.

Features

Installation

From CRAN

You can install the latest stable version of fastml from CRAN using:

install.packages("fastml")

You can install all dependencies (additional models) using:

# install all dependencies - recommended
install.packages("fastml", dependencies = TRUE)

From GitHub

For the development version, install directly from GitHub using the devtools package:

# Install devtools if you haven't already
install.packages("devtools")

# Install fastml from GitHub
devtools::install_github("selcukorkmaz/fastml")

Quick Start

Here’s a simple workflow to get you started with fastml:

library(fastml)

# Example dataset
data(iris)
iris <- iris[iris$Species != "setosa", ]  # Binary classification
iris$Species <- factor(iris$Species)

# Train models
model <- fastml(
  data = iris,
  label = "Species"
)

# View model summary
summary(model)

Tuning Strategies

fastml supports both grid search and Bayesian optimization through the tuning_strategy argument. Use "grid" for a regular parameter grid or "bayes" for Bayesian hyperparameter search. The tuning_iterations parameter controls the number of iterations only when tuning_strategy = "bayes" and is ignored otherwise.