Julia Machine Learning library
113 Stars
Updated Last
2 Years Ago
Started In
October 2013


Build Status Coverage Status Package Evaluator

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