divorce's Introduction

Rubix ML

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

Optional

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

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

Join Our Telegram Channel

Contributing

See CONTRIBUTING.md for guidelines.

License

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

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divorce's Issues

Create the tutorial!

We're looking for someone from the community to lay down some prose and help out your fellow developers. You'll have the chance to dive deep into the K Nearest Neighbors algorithm so that you can communicate its intuition to beginners who are just starting out using the Rubix ML library. You'll also have the freedom to use your creativity to develop a guide that is delightful and conveys the fundamentals of supervised classification, cross-validation, and data extraction.

Take a look at the other tutorials as a guide for structure and depth. The target audience is a beginner and the goal is to get them up and running quickly but leave breadcrumbs of information for them to explore later.

One suggestion is to include somewhere an intuitive explanation of KNN where the dataset is imagined in some n-dimensional Euclidean space and the classifier is locating nearest samples in such a space.

Why is KNN's confidence usually 1?

I have experimented quite a bit with this example over the past few days as an initial approach towards learning Rubix, and I have noticed that the confidence with which the KNN formulates its predictions is always 1 for the up class, and never in-between.

I would expect situations where the dominant class is not so clear, as the K neighbors may very well be mixed between the two.

I have also looked at the source code for the probaSample function and it looks correct to me. Have you experienced this result in your testing with the Divorce predictor? Could it be because of the nature of the problem, i.e., could the 54-dimensional feature vectors reside within well-defined boundaries for each of the two classes, married and divorced?

rubixml / divorce Goto Github PK