Prep at least an `.qmd` for end of first day about workshops HOT 4 CLOSED

We would want to mostly use models we've already talked about (decision tree, random forest) plus maybe one more.

from workshops.

Hello Julia, I want to tune the model using bayesian optimization by tidymodels but when defining the range of parameter values ​​there is a problem. Can you help me?

xgboost_spec <-
boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
loss_reduction = tune(), sample_size = tune(), mtry = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")

params_grid <- parameters(trees(range= seq(100, 2000, by=100 )), min_n(range=seq(10,40, by=5)), tree_depth(range = c(6:10)), learn_rate(range = seq(0.1,1, by=0.1)), loss_reduction(range = seq(0.1,1, by=0.1)),
sample_prop(range = seq(0.1,1, by=0.2)), finalize(mtry(range=seq(1,8,by=1)), data.training))

Error: range must have an upper and lower bound. Inf and unknown() are acceptable values.
Run rlang::last_error() to see where the error occurred.

from workshops.

@ararifuddinr Can you create a reprex (a minimal reproducible example) for this? The goal of a reprex is to make it easier for people to recreate your problem so that they can understand it and/or fix it. If you've never heard of a reprex before, you may want to start with the tidyverse.org help page.

Once you have a reprex, I recommend posting on RStudio Community, which is a great forum for getting help with these kinds of modeling questions. Thanks! 🙌

I'm going to hide these since they are not related to our workshop materials. 👍

from workshops.

I am very sorry for asking my problems that are not related to the material of this workshop. I've asked various platforms but can't find a solution. I tried to learn reprex according to your suggestion and made a question on stackoverflow.com. i hope you visit this site https://stackoverflow.com/questions/72641440/error-in-usemethodgrid-latin-hypercube-no-applicable-method-for-grid-lati Thank You.

from workshops.