details_boost_tree_mboost: Boosted trees

details_boost_tree_mboostR Documentation

Boosted trees

Description

mboost::blackboost() fits a series of decision trees forming an ensemble. Each tree depends on the results of previous trees. All trees in the ensemble are combined to produce a final prediction.

Details

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

Tuning Parameters

This model has 5 tuning parameters:

  • mtry: # Randomly Selected Predictors (type: integer, default: see below)

  • trees: # Trees (type: integer, default: 100L)

  • tree_depth: Tree Depth (type: integer, default: 2L)

  • min_n: Minimal Node Size (type: integer, default: 10L)

  • loss_reduction: Minimum Loss Reduction (type: double, default: 0)

The mtry parameter is related to the number of predictors. The default is to use all predictors.

Translation from parsnip to the original package (censored regression)

The censored extension package is required to fit this model.

library(censored)

boost_tree() %>% 
  set_engine("mboost") %>% 
  set_mode("censored regression") %>% 
  translate()
## Boosted Tree Model Specification (censored regression)
## 
## Computational engine: mboost 
## 
## Model fit template:
## censored::blackboost_train(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg(), family = mboost::CoxPH())

censored::blackboost_train() is a wrapper around mboost::blackboost() (and other functions) that makes it easier to run this model.

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.

Other details

Predictions of type "time" are predictions of the mean survival time.

References

  • Buehlmann P, Hothorn T. 2007. Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.

  • Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.


parsnip documentation built on Aug. 18, 2023, 1:07 a.m.