Focus is on the tidymodels book at: https://www.tmwr.org/. Main focus is just the basic structure of how a model is put together and the reasons behind various choices/engines. What are other options besides linear_reg()? What are different engines? Cover all the models that we use: linear, logistic, multinomial, ordered. Mention the dangers of overfitting in a couple of knowledge drops, but do not spend time in training/testing data. (Although we might consider a tidymodels-2 which did cover this stuff.) Some more notes:
Ordered outcomes
We no longer need to use polr. Instead:
parsnip has a new model type, ordinal_reg(), analogous to multinom_reg(), for fitting various generalized linear models with ordered class levels.
The ordered package by Cory Brunson is now on CRAN. This contains the specific engine code for these models, including:
ordinal_reg(): three engines: "polr", "ordinalNet", and "vglm".
gen_additive_mod(): "vgam"
decision_tree(): "rpartScore"
rand_forest(): "ordinalForest"
These models can be fitted, tuned, and evaluated with tidymodels. For the evaluation, we’ve added a specific performance metric for ordered categories: the ranked probability score (RPS). The function ranked_prob_score() is in the new yardstick release and requires an ordered factor for the outcome.
Focus is on the tidymodels book at: https://www.tmwr.org/. Main focus is just the basic structure of how a model is put together and the reasons behind various choices/engines. What are other options besides linear_reg()? What are different engines? Cover all the models that we use: linear, logistic, multinomial, ordered. Mention the dangers of overfitting in a couple of knowledge drops, but do not spend time in training/testing data. (Although we might consider a tidymodels-2 which did cover this stuff.) Some more notes:
Ordered outcomes
We no longer need to use polr. Instead: