Voir la documentation de bartProbit
The bartScore action scores a data table using a previously fitted Bayesian additive regression trees (BART) model. It generates predicted values, residuals, and confidence limits for each observation in the input data.
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.
Ajusta modelos de árboles de regresión aditivos bayesianos (BART) a datos de respuesta distribuidos normalmente.