rmw_model_nested_sets: Function to train random forest models using a nested tibble.

In skgrange/rmweather: Tools to Conduct Meteorological Normalisation and Counterfactual Modelling for Air Quality Data

Function to train random forest models using a nested tibble.

Description

Function to train random forest models using a nested tibble.

Usage

rmw_model_nested_sets(
  df_nest,
  variables,
  n_trees = 10,
  mtry = NULL,
  min_node_size = 5,
  n_cores = NA,
  verbose = FALSE
)

Arguments

df_nest	Nested tibble created by rmw_nest_for_modelling.
variables	Independent/explanatory variables used to predict "value".
n_trees	Number of trees to grow to make up the forest.
mtry	Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables.
min_node_size	Minimal node size.
n_cores	Number of CPU cores to use for the model calculations.
verbose	Should the function give messages?

Value

Nested tibble.

Author(s)

Stuart K. Grange

See Also

rmw_nest_for_modelling, rmw_predict_nested_sets, rmw_train_model

skgrange/rmweather documentation built on Nov. 29, 2023, 2:39 a.m.