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rubix/dota2
===========

Build a classifier to predict the outcome of Dota 2 games with the Naive Bayes algorithm and the results of 102,944 sample games.

v5(4y ago)161962MITPHPPHP >=7.4

Since Jun 18Pushed 11mo ago3 watchersCompare

[ Source](https://github.com/RubixML/Dota2)[ Packagist](https://packagist.org/packages/rubix/dota2)[ Docs](https://github.com/RubixML/Dota2)[ RSS](/packages/rubix-dota2/feed)WikiDiscussions master Synced today

READMEChangelog (5)Dependencies (1)Versions (6)Used By (0)

Rubix ML - Dota 2 Game Outcome Predictor
========================================

[](#rubix-ml---dota-2-game-outcome-predictor)

[Dota 2](http://www.dota2.com/) is a popular multiplayer online battle arena (MOBA) game that puts 10 players divided into 2 teams against each other. Each player controls a unique hero with abilities and its own set of strengths and weaknesses. Our objective is to build a classifier to predict the winning team based on hero matchup given a dataset of 102,944 individual matchups and their labeled outcomes. We'll employ the [Naive Bayes](https://rubixml.github.io/ML//latest/classifiers/naive-bayes.html) algorithm as our base estimator and learn how to save the trained model for use in another process. We'll also test the model to see how well it can generalize what it has learned to new data.

- **Difficulty:** Easy
- **Training time:** Minutes

Installation
------------

[](#installation)

Clone the project locally using [Composer](https://getcomposer.org/):

```
$ composer create-project rubix/dota2
```

Requirements
------------

[](#requirements)

- [PHP](https://php.net) 7.2 or above

#### Recommended

[](#recommended)

- 2G of system memory or more

Tutorial
--------

[](#tutorial)

On the map ...

Original Dataset
----------------

[](#original-dataset)

stephen.tridgell '@' sydney.edu.au

References
----------

[](#references)

> - Dua, D. and Graff, C. (2019). UCI Machine Learning Repository \[\]. Irvine, CA: University of California, School of Information and Computer Science.

License
-------

[](#license)

The code is licensed [MIT](LICENSE) and the tutorial is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).

###  Health Score

35

—

LowBetter than 77% of packages

Maintenance38

Infrequent updates — may be unmaintained

Popularity20

Limited adoption so far

Community10

Small or concentrated contributor base

Maturity60

Established project with proven stability

 Bus Factor1

Top contributor holds 100% of commits — single point of failure

How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. Uses a 2-year decay window.

**Popularity (30%)** — Total and monthly downloads, GitHub stars, and forks. Logarithmic scaling prevents top-heavy scores.

**Community (15%)** — Contributors, dependents, forks, watchers, and maintainers. Measures real ecosystem engagement.

**Maturity (30%)** — Project age, version count, PHP version support, and release stability.

###  Release Activity

Cadence

Every ~167 days

Total

5

Last Release

1539d ago

PHP version history (2 changes)v1PHP >=7.2

v4PHP >=7.4

### Community

Maintainers

![](https://www.gravatar.com/avatar/643b22cfe15a5f3ff42dc06ce98f1e5024b6e4578fc9627a058097f5046164d8?d=identicon)[andrewdalpino](/maintainers/andrewdalpino)

---

Top Contributors

[![andrewdalpino](https://avatars.githubusercontent.com/u/18690561?v=4)](https://github.com/andrewdalpino "andrewdalpino (21 commits)")

---

Tags

classifiercross-validationdata-sciencedotadota2machine-learningmachine-learning-tutorialnaive-bayesnaive-bayes-algorithmnaive-bayes-classifierphpphp-machine-learningphp-mlpredictionrubix-mlphpclassificationmachine learningmltutorialdatasetdata sciencenaive bayescross validationphp mlrubixmlrubix mlExample Projectdotadota 2game outcome prediciton

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