Tribuo

Abstract

1. Introduction

Tribuo is a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification. Tribuo provides implementations of popular ML algorithms and also wraps other libraries to provide a unified interface. Tribuo contains all the code necessary to load, featurise and transform data. Additionally, it includes the evaluation classes for all supported prediction types. Development is led by Oracle Labs' Machine Learning Research Group; we welcome community contributions.

All models and evaluations include a serializable provenance object which records the creation time of the model or evaluation, the identity of the data and any transformations applied to it, as well as the hyperparameters of the trainer. In the case of evaluations, this provenance information also includes the specific model used. Provenance information can be extracted as JSON, or serialised directly using Java serialisation. For production deployments, provenance information can be redacted and replaced with a hash to provide model tracking through an external system. Many Tribuo models can be exported in ONNX format for deployment in other languages, platforms or cloud services.

Tribuo runs on Java 8+, and we test on LTS versions of Java along with the latest release. Tribuo itself is a pure Java library and is supported on all Java platforms; however, some of our interfaces require native code and are thus supported only where there is native library support. We test on x86\_64 architectures on Windows 10, macOS and Linux (RHEL/OL/CentOS 7+), as these are supported platforms for the native libraries with which we interface. If you're interested in another platform and wish to use one of the native library interfaces (ONNX Runtime, TensorFlow, and XGBoost), we recommend reaching out to the developers of those libraries. Note the model card and reproducibility packages require Java 17, and as such are not part of the tribuo-all Maven Central deployment.

2. Objective

Tribuo - A Java machine learning library

This project demonstrates how Java, Machine Learning, Deep Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• JDK 11 or later
• Maven / Gradle
• IntelliJ IDEA, Eclipse or Android Studio
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/oracle/tribuo.git
cd tribuo

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by oracle and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.