hfrick

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hfrick

library(tidyverse)
library(tidymodels)

otters_raw <- read_csv("seot_morphometricsReproStatus_ak_monson.csv") %>% 
  janitor::clean_names()

otters <- otters_raw %>% 
  mutate(
    final_age = if_else(final_age == -9, NA_real_, final_age),
    weight = if_else(weight == -9, NA_real_, weight),

hfrick

More thoughts on data sets for post-processing

@simonpcouch and I have been brainstorming with regard to how and where to specify and make the dataset used to estimate a post-processor, currently dubbed the "potato set".

What to specify

• The proportion of the data used for estimatation (preproc, model, post) that should be held back specificially for estimating the post-processor.
• The method for how to split that data for estimation. This may need to be a time-based or grouped split rather than a random split. If we are in the context of resampling a workflow, it should most likely be the same method as used to make the resamples.

Where to specify