In topepo/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("rand_forest", "aorsf")

Tuning Parameters

defaults <-
  tibble::tibble(parsnip = c("trees", "min_n", "mtry"),
                 default = c("500L", "5L", "ceiling(sqrt(n_predictors))"))

param <-
  rand_forest() %>%
  set_engine("aorsf") %>%
  set_mode("censored regression") %>%
  make_parameter_list(defaults) %>%
  distinct()

This model has r nrow(param) tuning parameters:

param$item

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

• split_min_stat: Minimum test statistic required to split a node. Default is 3.841459 for the log-rank test, which is roughly a p-value of 0.05.

Translation from parsnip to the original package (censored regression)

r uses_extension("rand_forest", "aorsf", "censored regression")

library(censored)

rand_forest() %>%
  set_engine("aorsf") %>%
  set_mode("censored regression") %>%
  translate()

Preprocessing requirements

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

• Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique random survival forests. Annals of applied statistics 2019 Sep; 13(3):1847-83. DOI: 10.1214/19-AOAS1261

• Jaeger BC, Welden S, Lenoir K, Pajewski NM. aorsf: An R package for supervised learning using the oblique random survival forest. Journal of Open Source Software 2022, 7(77), 1 4705. \doi{10.21105/joss.04705}.

• Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM. Accelerated and interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129

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