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credit's Introduction

Rubix ML

PHP from Packagist Latest Stable Version Downloads from Packagist Code Checks GitHub

A high-level machine learning and deep learning library for the PHP language.

  • Developer-friendly API is delightful to use
  • 40+ supervised and unsupervised learning algorithms
  • Support for ETL, preprocessing, and cross-validation
  • Open source and free to use commercially

Installation

Install Rubix ML into your project using Composer:

$ composer require rubix/ml

Requirements

  • PHP 7.4 or above

Recommended

Optional

Documentation

Read the latest docs here.

What is Rubix ML?

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 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.

Getting Started

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.

Tutorials & Example Projects

Check out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.

Interact With The Community

Contributing

See CONTRIBUTING.md for guidelines.

License

The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.

credit's People

Contributors

andrewdalpino avatar mickaelandrieu avatar

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credit's Issues

contamination / information leakage between training and testing data

I notice that both in the README.md and train.php the same mistake is made:

Namely that ZScaleStandardizer is used BEFORE the train test split and not AFTER. This results in information leakage right from the start and puts into question all the various metrics at the end.

The correct approach would be using ZScaleStandardizer on the training set only and capturing it's parameters to repeat on the testing set before using the trained model to make predictions.

This can potentially mislead newer users studying machine learning into bad habits that will later need to be unlearned.

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