details_rand_forest_aorsf: Oblique random survival forests via aorsf

In parsnip: A Common API to Modeling and Analysis Functions

Oblique random survival forests via aorsf

Description

aorsf::orsf() fits a model that creates a large number of decision trees, each de-correlated from the others. The final prediction uses all predictions from the individual trees and combines them.

Details

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

Tuning Parameters

This model has 3 tuning parameters:

• trees: # Trees (type: integer, default: 500L)

• min_n: Minimal Node Size (type: integer, default: 5L)

• mtry: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors)))

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)

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

• 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. .

• 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

parsnip documentation built on June 24, 2024, 5:14 p.m.