man/rmd/rand_forest_aorsf.md

For this engine, there is a single mode: censored regression

Tuning Parameters

This model has 3 tuning parameters:

Additionally, this model has one engine-specific tuning parameter:

Translation from parsnip to the original package (censored regression)

The censored extension package is required to fit this model.

library(censored)

rand_forest() %>%
  set_engine("aorsf") %>%
  set_mode("censored regression") %>%
  translate()
## Random Forest Model Specification (censored regression)
## 
## Computational engine: aorsf 
## 
## Model fit template:
## aorsf::orsf(formula = missing_arg(), data = missing_arg(), weights = missing_arg())

Preprocessing requirements

This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. {a, c} vs {b, d}) when splitting at a node. Dummy variables are not required for this model.

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in [case_weights] and the examples on tidymodels.org.

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of case weights.

Other details

Predictions of survival probability at a time exceeding the maximum observed event time are the predicted survival probability at the maximum observed time in the training data.

References



topepo/parsnip documentation built on April 16, 2024, 3:23 a.m.