Random forests via randomForest

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

Details

For this engine, there are multiple modes: classification and regression

Tuning Parameters

This model has 3 tuning parameters:

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

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

• min_n: Minimal Node Size (type: integer, default: see below)

mtry depends on the number of columns and the model mode. The default in randomForest::randomForest() is floor(sqrt(ncol(x))) for classification and floor(ncol(x)/3) for regression.

min_n depends on the mode. For regression, a value of 5 is the default. For classification, a value of 10 is used.

Translation from parsnip to the original package (regression)

rand_forest(
  mtry = integer(1),
  trees = integer(1),
  min_n = integer(1)
) |>
  set_engine("randomForest") |>
  set_mode("regression") |>
  translate()

## Random Forest Model Specification (regression)
##
## Main Arguments:
##   mtry = integer(1)
##   trees = integer(1)
##   min_n = integer(1)
##
## Computational engine: randomForest
##
## Model fit template:
## randomForest::randomForest(x = missing_arg(), y = missing_arg(),
##     mtry = min_cols(~integer(1), x), ntree = integer(1), nodesize = min_rows(~integer(1),
##         x))

min_rows() and min_cols() will adjust the number of neighbors if the chosen value if it is not consistent with the actual data dimensions.

Translation from parsnip to the original package (classification)

rand_forest(
  mtry = integer(1),
  trees = integer(1),
  min_n = integer(1)
) |>
  set_engine("randomForest") |>
  set_mode("classification") |>
  translate()

## Random Forest Model Specification (classification)
##
## Main Arguments:
##   mtry = integer(1)
##   trees = integer(1)
##   min_n = integer(1)
##
## Computational engine: randomForest
##
## Model fit template:
## randomForest::randomForest(x = missing_arg(), y = missing_arg(),
##     mtry = min_cols(~integer(1), x), ntree = integer(1), nodesize = min_rows(~integer(1),
##         x))

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.

Note that the data passed to the case.weights column are not used for traditional case weights (where the objective function is multiplied by a row-specific weight). From ?randomForest::randomForest: “A vector of length same asy that are positive weights used only in sampling data to grow each tree (not used in any other calculation).”

They function as sampling weights.

Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.

Prediction types

parsnip:::get_from_env("rand_forest_predict") |>
  dplyr::filter(engine == "randomForest") |>
  dplyr::select(mode, type)

## # A tibble: 5 x 2
##   mode           type
##   <chr>          <chr>
## 1 regression     numeric
## 2 regression     raw
## 3 classification class
## 4 classification prob
## 5 classification raw

Examples

The “Fitting and Predicting with parsnip” article contains examples for rand_forest() with the "randomForest" engine.

References

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