The grf fits models that create a large number of decision trees, each independent of the others. The final prediction uses predictions from the individual trees and combines them.
Details
For this engine, there are multiple modes: classification, regression, and quantile regression
Tuning Parameters
This model has 3 tuning parameters:
mtry: # Randomly Selected Predictors (type: integer, default: see below)trees: # Trees (type: integer, default: 2000L)min_n: Minimal Node Size (type: integer, default: 5L)
mtry depends on the number of columns. If there are p predictors,
the default value of mtry is min(ceiling(sqrt(p) + 20), p).
Translation from parsnip to the original package (regression)
rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) |>
set_engine("grf") |>
set_mode("regression") |>
translate()## Random Forest Model Specification (regression)
##
## Main Arguments:
## mtry = integer(1)
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: grf
##
## Model fit template:
## grf::regression_forest(X = missing_arg(), Y = missing_arg(),
## weights = missing_arg(), mtry = min_cols(~integer(1), x),
## num.trees = integer(1), min.node.size = min_rows(~integer(1),
## x), num.threads = 1)Translation from parsnip to the original package (classification)
rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) |>
set_engine("grf") |>
set_mode("classification") |>
translate()## Random Forest Model Specification (classification)
##
## Main Arguments:
## mtry = integer(1)
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: grf
##
## Model fit template:
## grf::probability_forest(X = missing_arg(), Y = missing_arg(),
## weights = missing_arg(), mtry = min_cols(~integer(1), x),
## num.trees = integer(1), min.node.size = min_rows(~integer(1),
## x), num.threads = 1)Translation from parsnip to the original package (quantile regression)
See
?quantile_forest
When specifying any quantile regression model, the user must specify the quantile levels a priori.
rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) |>
set_engine("grf") |>
set_mode("quantile regression", quantile_levels = (1:3) / 4) |>
translate()## Random Forest Model Specification (quantile regression)
##
## Main Arguments:
## mtry = integer(1)
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: grf
##
## Model fit template:
## grf::quantile_forest(X = missing_arg(), Y = missing_arg(), mtry = min_cols(~integer(1),
## x), num.trees = integer(1), min.node.size = min_rows(~integer(1),
## x), num.threads = 1, quantiles = quantile_levels)
## Quantile levels: 0.25, 0.5, and 0.75.Preprocessing requirements
This method does require qualitative predictors to be converted to a numeric format (manually). When using parsnip, a one-hot encoding is automatically used to do this.
If there are missing values in the predictors, the model will use case-wise deletion to remove them.
Other notes
By default, parallel processing is turned off. When tuning, it is more
efficient to parallelize over the resamples and tuning parameters. To
parallelize the construction of the trees within the grf model, change
the num.threads argument via set_engine().
For grf confidence intervals, the intervals are constructed using the
form estimate +/- z * std_error. For classification probabilities,
these values can fall outside of [0, 1] and will be coerced to be in
this range.
Case weights
The regression and classification models enable the use of case weights. The quantile regression mode does not.
Examples
The “Fitting and Predicting with parsnip” article contains
examples
for rand_forest() with the "grf" engine.