An easy-to-use but powerful and fast machine learning library for Java

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Come say "Hi!" on our Discussion Forum, or improve QuickML and submit a pull request through GitHub. Want to help? Take a look at our open issues and see if there is anything you can tackle, fork our code, then submit a pull request.

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Read the current release's JavaDocs here (courtesy of JitPack.io).

Documentation of QuickML is currently lagging significantly behind functionality, but the API is fairly well defined and should be mostly self-explanatory using the examples below as a starting point, just start poking around the source code, particularly the unit tests. You can also improve this site by editing this file.

Please also note that the QuickML API remains subject to change, and will continue to do so until 1.0 (although we will only make such changes if we're convinced they're necessary to make the API as good as it can be).

How to use


Add the following repository to your Maven pom.xml file:

Add the following dependency to your Maven pom.xml file:
  <version>Find latest release here</version>

Build a simple predictive model

Here we train a random forest on the well-known Fisher Iris dataset, which is included with QuickML for your convenience. We then generate a prediction for a particular set of input attributes:

import quickml.data.*;
import quickml.supervised.classifier.randomForest.*;
// ...
List<ClassifierInstance> irisDataset = PredictiveAccuracyTests.loadIrisDataset();
final RandomForest randomForest = new RandomForestBuilder().buildPredictiveModel(irisDataset);
AttributesMap attributes = new AttributesMap();
attributes.put("sepal-length", 5.84);
attributes.put("sepal-width", 3.05);
attributes.put("petal-length", 3.76);
attributes.put("petal-width", 1.2);
System.out.println("Prediction: " + randomForest.getProbability(attributes, "Iris-virginica"));


QuickML was initially created by Ian Clarke, and has had significant contributions by Alex Hawk, Chris Reeves, and Michael Kelly of OneSpot.