Julia Machine Learning library

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Last Touched

about 1 month ago

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350 commits



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The MachineLearning package represents the very beginnings of an attempt to consolidate common machine learning algorithms written in pure Julia and presenting a consistent API. Initially, the package will be targeted towards the machine learning practitioner, working with a dataset that fits in memory on a single machine. Longer term, I hope this will both target much larger datasets and be valuable for state of the art machine learning research as well.

API Introduction

model = [2.0,1.0,-1.0]
x_train = randn(1_000, 3)
y_train = int(map(x->x>0, x_train*model))
net = fit(x_train, y_train, classification_net_options())
sample = [1.0, 0.0, 0.0]
println("Ground truth: ", int(dot(sample,model)>0))
println("Prediction:   ", predict(net, sample))

Algorithms Implemented

  • Basic Decision Tree for Classification
  • Basic Random Forest for Classification
  • Basic Neural Network
  • Bayesian Additive Regression Trees

Other Helpers

  • Train/Test split
  • Cross validation
  • Experiments