Differentiable tree-based models for tabular data.
| Documentation | CI Status | DOI |
|---|---|---|
 |
 |
 |
] add NeuroTreeModels⚠ Compatible with Julia >= v1.9.
A model configuration is defined with on of the constructor:
using NeuroTreeModels, DataFrames
config = NeuroTreeRegressor(
loss = :mse,
nrounds = 10,
num_trees = 16,
depth = 5,
)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)p = m(dtrain)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)Benchmarking against prominent ML libraries for tabular is performed at MLBenchmarks.jl.