Voir la documentation de bartGauss
The bartProbit action fits a probit Bayesian Additive Regression Trees (BART) model to data where the response variable is binary. This is particularly useful for classification problems where the outcome is one of two categories (e.g., yes/no, success/failure, 0/1). The probit model assumes that the binary outcome is the result of an unobserved continuous latent variable following a standard normal distribution. The BART model itself is a non-parametric, ensemble method that combines multiple simple regression trees to create a powerful predictive model, offering a flexible alternative to traditional parametric models.
Ajusta modelos de árboles de regresión aditivos bayesianos (BART) probit a datos de respuesta con distribución binaria.
Calcula los márgenes predictivos utilizando un modelo de árboles de regresión aditivos bayesianos (BART) ajustado.