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.

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))

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

• Train/Test split
• Cross validation
• Experiments