Outcome / partial residual used to sample an additive model. The outcome class is a wrapper around a vector of (mutable) outcomes for ML tasks (supervised learning, causal inference). When an additive tree ensemble is sampled, the outcome used to sample a specific model term is the "partial residual" consisting of the outcome minus the predictions of every other model term (trees, group random effects, etc...).
This class is intended for advanced use cases in which users require detailed control of sampling algorithms and data structures. Minimal input validation and error checks are performed – users are responsible for providing the correct inputs. For tutorials on the "proper" usage of the stochtree's advanced workflow, we provide several vignettes at https://stochtree.ai/
Methods
Method add_vector()
Update the current state of the outcome (i.e. partial residual) data by adding the values of update_vector
Method subtract_vector()
Update the current state of the outcome (i.e. partial residual) data by subtracting the values of update_vector