Welcome to Random-ML’s Documentation!

`random-ml` is a machine learning package focused on randomized neural networks and functional link architectures. It includes Random Vector Functional Link (RVFL) networks, ensemble learning techniques, and various randomized machine learning models.

What is Random-ML?

Traditional neural networks rely on gradient-based learning for optimization. However, randomized networks like RVFL use fixed random weights for hidden layers, allowing: - Faster Training (no backpropagation) - Closed-Form Solutions (Ridge Regression, Moore-Penrose Pseudoinverse) - Good Generalization Performance

Install

The easiest way to install random-ml is to use PyPI by running:

pip install random-ml

Alternatively, github can be forked and cloned to install the package.

Reference

The work is a part of MLPedRVFL paper, The preprint is submitted to Pattern Recognition Letter

API Reference

The reference guide contains a detailed description of the scikit-survival API. It describes which classes and functions are available and what their parameters are.

Contributing

The package is in an early phase of development and we welcome contributions from the community. This guide explains how to contribute to the project.

Key Features

• ✅ RVFL Variants (Basic RVFL, Extended RVFL, and MLPedRVFL)

• ✅ Ensemble Learning → Bagging & Boosting for RVFL

• ✅ Flexible Activation Functions (relu, sigmoid, tanh, etc.)

• ✅ Direct Link Option (For RVFL models)

Future Plans

• 📈 More Randomized Models (ELM, SCN, etc.)

• 📈 Deep Randomized Networks (Randomized Deep Learning)

• 📈 Optimization Techniques (Particle Swarm Optimization, Genetic Algorithms)

• 📈 Model Interpretability (Feature Importance, SHAP Values)

• 📈 Model Selection (Cross-Validation, Hyperparameter Tuning)

• 📈 Model Evaluation (Metrics, Plots, etc.)

• 📈 Model Deployment (Serialization, Web APIs, etc.)