create_skeleton {forecastML} | R Documentation |
Remove the features from a lagged training dataset to reduce memory consumption
Description
create_skeleton()
strips the feature data from a create_lagged_df()
object
but keeps the outcome column(s), any grouping columns, and meta-data which allows the resulting
lagged_df
to be used downstream in the forecastML
pipeline. The main benefit is
that the custom modeling function passed in train_model()
can read data directly from the
disk or a database when the dataset is too large to fit into memory.
Usage
create_skeleton(lagged_df)
Arguments
lagged_df |
An object of class 'lagged_df' from |
Value
An S3 object of class 'lagged_df' or 'grouped_lagged_df': A list of data.frames with the
outcome column(s) and any grouping columns but with all other features removed.
A special attribute skeleton = TRUE
is added.
Methods and related functions
The output of create_skeleton
can be passed into
[Package forecastML version 0.9.0 Index]