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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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.