quantlab.model

Modules

base	Model layer: the training lifecycle shared by every model head.
collected_panel	The collected panel: what a model's collect() read, and the arrays built from it.
config	The config of the model layer.
ensemble	The base class of every ensemble of models, shipped or user-written.
evaluation	Evaluation: score a trained unit's predictions against its raw labels.
library_model	Library variant of the model layer: LibraryModel and its rows.
predefined
split	Splits of a timestamp axis: purged segments, in/out-of-sample ranges and walk-forward folds.
torch_data	The data a TorchModel head is fed: the training panel, batches and datasets.
torch_model	Torch variant of the model layer: TorchModel, the base class of every torch head.
torch_training	Training pieces a TorchModel head may pick for its hooks.
training_target	The training target, computed once per bar before a fit: shared by both model variants.
view	Predictor views: predictors that answer through the predictor they wrap.
walk_forward_training	Walk-forward training: one walk-forward cross-validation of a model or an ensemble.