README.md

NeuroTreeModels.jl

Differentiable tree-based models for tabular data.

Documentation	CI Status	DOI

Installation

] add NeuroTreeModels

⚠ Compatible with Julia >= v1.9.

Configuring a model

A model configuration is defined with on of the constructor:

• NeuroTreeRegressor
• NeuroTreeClassifier

using NeuroTreeModels, DataFrames

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

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.

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

p = m(dtrain)

MLJ

NeuroTreeModels.jl supports the MLJ Interface.

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 is performed at MLBenchmarks.jl.