View source: R/modeltime-fit-workflowset.R
create_model_grid | R Documentation |
parsnip
model specs from a dials
parameter gridHelper to make parsnip
model specs from a dials
parameter grid
create_model_grid(grid, f_model_spec, engine_name, ..., engine_params = list())
grid |
A tibble that forms a grid of parameters to adjust |
f_model_spec |
A function name (quoted or unquoted) that
specifies a |
engine_name |
A name of an engine to use. Gets passed to |
... |
Static parameters that get passed to the f_model_spec |
engine_params |
A |
This is a helper function that combines dials
grids with
parsnip
model specifications. The intent is to make it easier
to generate workflowset
objects for forecast evaluations
with modeltime_fit_workflowset()
.
The process follows:
Generate a grid (hyperparemeter combination)
Use create_model_grid()
to apply the parameter combinations to
a parsnip model spec and engine.
The output contains ".model" column that can be used as a list
of models inside the workflow_set()
function.
Tibble with a new colum named .models
dials::grid_regular()
: For making parameter grids.
workflowsets::workflow_set()
: For creating a workflowset
from the .models
list stored in the ".models" column.
modeltime_fit_workflowset()
: For fitting a workflowset
to forecast data.
library(tidymodels) library(modeltime) # Parameters that get optimized grid_tbl <- grid_regular( learn_rate(), levels = 3 ) # Generate model specs grid_tbl %>% create_model_grid( f_model_spec = boost_tree, engine_name = "xgboost", # Static boost_tree() args mode = "regression", # Static set_engine() args engine_params = list( max_depth = 5 ) )
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