Integration with MLJ

Maxnet.jl integrates with the MLJ ecosystem.

See MLJs project page for more info about MLJ.

To use Maxnet with MLJ, initialise a model by calling MaxnetBinaryClassifier, which accepts any arguments otherwise passed to maxnet. The model can then be used with MLJ's machine.

For example:

using Maxnet: MaxnetBinaryClassifier, bradypus
using MLJBase

# sample data
y, X = bradypus()

# define a model
model = MaxnetBinaryClassifier(features = "lq")

# construct a machine
mach = machine(model, X, categorical(y))

# partition data
train, test = partition(eachindex(y), 0.7, shuffle=true)

# fit the machine to the data
fit!(mach; rows = train)

# predict on test data
pred_test = predict(mach; rows = test)

# predict on some new dataset
pred = predict(mach, X)