Voir la documentation de bartProbit
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.
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.
Ajusta modelos de árboles de regresión aditivos bayesianos (BART) probit a datos de respuesta con distribución binaria.