API

Training

MLJModelInterface.fit Function

fit(
    config::LearnerTypes,
    dtrain;
    feature_names,
    target_name,
    weight_name=nothing,
    offset_name=nothing,
    deval=nothing,
    metric=nothing,
    print_every_n=9999,
    early_stopping_rounds=9999,
    verbosity=1,
)

Training function of NeuroTabModels' internal API.

Arguments

• config::LearnerTypes: The configuration object defining the model architecture, loss, and training hyperparameters.

• dtrain: The training data. Must be <:AbstractDataFrame.

Keyword arguments

• feature_names: Required. A Vector{Symbol} or Vector{String} of the feature names to use.

• target_name: Required. A Symbol or String indicating the name of the target variable.

• weight_name=nothing: Optional. A Symbol or String indicating the sample weights column.

• offset_name=nothing: Optional. A Symbol or String indicating the offset column.

• deval=nothing: Optional. Evaluation data (<:AbstractDataFrame) for tracking metrics and early stopping.

• metric=nothing: Optional. The evaluation metric to track (e.g., :mse, :logloss).

• print_every_n=9999: Integer. Logs training progress to the console every N epochs.

• early_stopping_rounds=9999: Integer. Stops training if the evaluation metric does not improve for this many rounds.

• verbosity=1: Integer. Controls the logging level (0 for silent, >0 for info).

• device=:cpu: Symbol. Hardware device to use for training (:cpu or :gpu).

• gpuID=0: Integer. Specifies which GPU to use if multiple are available.

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Inference

NeuroTabModels.Infer.infer Function

infer(m::NeuroTabModel, data; device=:cpu, backend=get(m.info, :backend, :zygote), proj=true)

Run inference on batched feature data.

Arguments

• m::NeuroTabModel: A fitted model.

• data: Iterable of feature batches.

Keyword arguments

• device=:cpu: Execution device (:cpu or :gpu).

• backend: AD backend (:zygote, :enzyme, or :reactant). Defaults to the value stored on the model.

• proj=true: When true, map raw outputs to natural scale. Set to false for raw model-scale predictions.

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infer(m::NeuroTabModel, df::AbstractDataFrame; device=:cpu, backend=get(m.info, :backend, :zygote), proj=true)

Run inference on tabular data and return predictions.

Arguments

• m::NeuroTabModel: A fitted model.

• df: Feature data as an AbstractDataFrame.

Keyword arguments

• device=:cpu: Execution device (:cpu or :gpu).

• backend: AD backend (:zygote, :enzyme, or :reactant). Defaults to the value stored on the model.

• proj=true: When true, map raw outputs to natural scale. Set to false for raw model-scale outputs.

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