NeuroTreeModels

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Getting started with NeuroTreeModels.jl ​

Installation ​

julia
] add NeuroTreeModels

Configuring a model ​

A model configuration is defined with the NeuroTreeRegressor constructor:

julia
using NeuroTreeModels, DataFrames

config = NeuroTreeRegressor(
    loss = :mse,
    nrounds = 10,
    num_trees = 16,
    depth = 5,
    device = :cpu
)

For training on GPU, use device=:gpu in the constructor, and optionally gpuID=0 to target a specific a device.

Training ​

Building and training a model according to the above config is done with NeuroTreeModels.fit. See the docs for additional features, notably early stopping support through the tracking of an evaluation metric.

julia
nobs, nfeats = 1_000, 5
dtrain = DataFrame(randn(nobs, nfeats), :auto)
dtrain.y = rand(nobs)
feature_names, target_name = names(dtrain, r"x"), "y"

m = NeuroTreeModels.fit(config, dtrain; feature_names, target_name)

Inference ​

julia
p = m(dtrain)
p = m(dtrain; device=:gpu)

MLJ ​

NeuroTreeModels.jl supports the MLJ Interface.

julia
using MLJBase, NeuroTreeModels
m = NeuroTreeRegressor(depth=5, nrounds=10)
X, y = @load_boston
mach = machine(m, X, y) |> fit!
p = predict(mach, X)

Benchmarks ​

Benchmarking against prominent ML libraries for tabular data is performed at MLBenchmarks.jl.