sparklyr::ml_random_forest() 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: 20L)

  • min_n: Minimal Node Size (type: integer, default: 1L)

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

Translation from parsnip to the original package (regression)

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

## Random Forest Model Specification (regression)
## 
## Main Arguments:
##   mtry = integer(1)
##   trees = integer(1)
##   min_n = integer(1)
## 
## Computational engine: spark 
## 
## Model fit template:
## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(), 
##     type = "regression", feature_subset_strategy = integer(1), 
##     num_trees = integer(1), min_instances_per_node = min_rows(~integer(1), 
##         x), seed = sample.int(10^5, 1))

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("spark") %>% 
  set_mode("classification") %>% 
  translate()

## Random Forest Model Specification (classification)
## 
## Main Arguments:
##   mtry = integer(1)
##   trees = integer(1)
##   min_n = integer(1)
## 
## Computational engine: spark 
## 
## Model fit template:
## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(), 
##     type = "classification", feature_subset_strategy = integer(1), 
##     num_trees = integer(1), min_instances_per_node = min_rows(~integer(1), 
##         x), seed = sample.int(10^5, 1))

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

For models created using the "spark" engine, there are several things to consider.

  • Only the formula interface to via fit() is available; using fit_xy() will generate an error.

  • The predictions will always be in a Spark table format. The names will be the same as documented but without the dots.

  • There is no equivalent to factor columns in Spark tables so class predictions are returned as character columns.

  • To retain the model object for a new R session (via save()), the model$fit element of the parsnip object should be serialized via ml_save(object$fit) and separately saved to disk. In a new session, the object can be reloaded and reattached to the parsnip object.

References

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