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Home Page: https://conf20-intro-ml.netlify.com/
License: Creative Commons Attribution Share Alike 4.0 International
Intro to Machine Learning with the Tidyverse
Home Page: https://conf20-intro-ml.netlify.com/
License: Creative Commons Attribution Share Alike 4.0 International
Right before "Your turn 2" in the 01-prediction slides (https://conf20-intro-ml.netlify.com/materials/01-predicting/)
You use the custom fit_dat
function to fit the lm model
lm_spec <-
linear_reg() %>% # Pick linear regression
set_engine(engine = "lm") # set engine
fit_data(Sale_Price ~ Gr_Liv_Area, model = lm_spec, data = ames)
You can also do it straight in parsnip
now without using the custom fit_data
function now
lm_spec <- parsnip::linear_reg() %>%
parsnip::set_engine(engine = "lm")
parsnip::fit(lm_spec, Sale_Price ~ Gr_Liv_Area, data = ames)
It's even pipe-able!
Not sure if this is a new feature that was added or just to stay consistent with the trees/random forest slides later on.
option(scipen=17)
When passing a workflow object to tune::collect_metrics()
the resulting error message is Error: All of the models failed.
(see attached example)
A more informative error message would remind the user to pass a fit_split
object instead.
Thanks!
In the 06-recipes
slides:
I think this should've read ames_training
instead of just ames.
There are a lot of slides before it that use ames
where it should be using ames_training
, but this example gets built on to train the model, so it seems especially important here.
#18 is also related
uses ames
instead of ames_train
cc @JosiahParry
This is one of my favorite blog posts that talk about the various regression metrics.
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