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

Comprehensive Data Preprocessing: Handle missing values, encode categorical variables, and apply various scaling methods with minimal code.
Support for Multiple Algorithms: Train a wide array of machine learning models including XGBoost, Random Forest, SVMs, KNN, Neural Networks, and more.
Hyperparameter Tuning: Customize and automate hyperparameter tuning for each algorithm to optimize model performance.
Performance Evaluation: Evaluate models using metrics like Accuracy, Kappa, Sensitivity, Specificity, Precision, F1 Score, and ROC AUC.
Visualization Tools: Generate comparison plots to visualize and compare the performance of different models effortlessly.
Easy Integration: Designed to integrate seamlessly into your existing R workflows with intuitive function interfaces.

Installation

From CRAN

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

install.packages("fastml")

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)