Voir la documentation de bartGauss
The bartScoreMargin action computes predictive margins by using a fitted Bayesian additive regression trees (BART) model. Predictive margins are predictions from a model at fixed values of some predictors, averaged over the distribution of the other predictors. This technique is useful for understanding the effect of a specific predictor on the outcome, while accounting for the influence of other variables in the model.
The bartGauss action fits Bayesian additive regression trees (BART) models for a continuous response variable that is assumed to follow a normal distribution. BART is a non-parametric regression method that uses a sum of regression trees to model the relationship between predictors and a response. It is particularly effective for capturing complex, non-linear relationships and interactions in the data without requiring pre-specification of the model form. The method is Bayesian, meaning it uses priors for the model parameters and provides a full posterior distribution for predictions, allowing for robust uncertainty quantification.
Crea una tabla en el servidor que contiene los resultados de la puntuación de las observaciones utilizando un modelo ajustado de árboles de regresión aditiva bayesianos (BART). Esta acción es fundamental para aplicar un modelo BART entrenado a nuevos datos para generar predicciones.