A high-level machine learning and deep learning library for the PHP language.
- Developer-friendly API is delightful to use
- 50+ supervised and unsupervised learning algorithms
- Support for ETL, preprocessing, and cross-validation
- Open source and free to use commercially
Install Rubix ML into your project using Composer:
composer require rubix/ml- PHP 7.4 or above
- Tensor extension for fast Matrix/Vector computing
- GD extension for image support
- Mbstring extension for fast multibyte string manipulation
- SVM extension for Support Vector Machine engine (libsvm)
- PDO extension for relational database support
- GraphViz for graph visualization
Read the latest docs here.
Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 50 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.
The following algorithms have been added to bring Rubix ML closer to scikit-learn parity:
| Algorithm | Type | Highlights |
|---|---|---|
| Elastic Net | Regressor | Combined L1 + L2 regularisation via coordinate descent |
| Gaussian Process Regressor | Regressor | Bayesian non-parametric regression with posterior uncertainty |
| Factorization Machine | Regressor | O(kn) pairwise feature interactions, online SGD |
| Hidden Markov Model | Classifier | Per-class Gaussian HMM, Baum-Welch EM training |
| Factorization Machine | Classifier | Probabilistic, multi-class, online SGD |
| Online SGD Classifier | Classifier | AdaGrad, log / hinge / perceptron losses, streaming-safe |
| Hoeffding Tree Classifier | Classifier | VFDT — learns from infinite streams without storing data |
| Spectral Clustering | Clusterer | Graph Laplacian eigenmap + K-Means, auto-detects non-convex clusters |
| UMAP | Transformer | Fuzzy topological manifold embedding, faster and more structure-preserving than t-SNE |
If you are new to machine learning, we recommend taking a look at the What is Machine Learning? section to get started. If you are already familiar with basic ML concepts, you can browse the basic introduction for a brief look at a typical Rubix ML project. From there, you can browse the official tutorials below which range from beginner to advanced skill level.
Check out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.
- CIFAR-10 Image Recognizer
- Color Clusterer
- Credit Default Risk Predictor
- Customer Churn Predictor
- Divorce Predictor
- DNA Taxonomer
- Dota 2 Game Outcome Predictor
- Human Activity Recognizer
- Housing Price Predictor
- Iris Flower Classifier
- MNIST Handwritten Digit Recognizer
- Text Sentiment Analyzer
- Titanic Survival Predictor
See CONTRIBUTING.md for guidelines.
The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.