Nothing
Code
combine_words(1)
Output
1
Code
combine_words(1:2)
Output
1 and 2
Code
combine_words(1:3)
Output
1, 2, and 3
Code
combine_words(1:4)
Output
1, 2, 3, and 4
Code
bag_tree() %>% set_engine("rpart") %>% set_mode("regression")
Message
! parsnip could not locate an implementation for `bag_tree` regression model specifications using the `rpart` engine.
i The parsnip extension package baguette implements support for this specification.
i Please install (if needed) and load to continue.
Output
Bagged Decision Tree Model Specification (regression)
Main Arguments:
cost_complexity = 0
min_n = 2
Computational engine: rpart
Code
bag_tree() %>% set_mode("censored regression")
Message
! parsnip could not locate an implementation for `bag_tree` censored regression model specifications.
i The parsnip extension package censored implements support for this specification.
i Please install (if needed) and load to continue.
Output
Bagged Decision Tree Model Specification (censored regression)
Main Arguments:
cost_complexity = 0
min_n = 2
Computational engine: rpart
Code
bag_tree()
Message
! parsnip could not locate an implementation for `bag_tree` model specifications.
i The parsnip extension packages censored and baguette implement support for this specification.
i Please install (if needed) and load to continue.
Output
Bagged Decision Tree Model Specification (unknown mode)
Main Arguments:
cost_complexity = 0
min_n = 2
Computational engine: rpart
Code
bag_tree() %>% set_engine("rpart")
Message
! parsnip could not locate an implementation for `bag_tree` model specifications using the `rpart` engine.
i The parsnip extension packages censored and baguette implement support for this specification.
i Please install (if needed) and load to continue.
Output
Bagged Decision Tree Model Specification (unknown mode)
Main Arguments:
cost_complexity = 0
min_n = 2
Computational engine: rpart
Code
bt
Message
! parsnip could not locate an implementation for `bag_tree` model specifications.
i The parsnip extension packages censored and baguette implement support for this specification.
i Please install (if needed) and load to continue.
Output
Bagged Decision Tree Model Specification (regression)
Main Arguments:
cost_complexity = 0
min_n = 2
Computational engine: rpart
Code
set_engine(mtcars, "rpart")
Condition
Error in `set_engine()`:
! `set_engine()` expected a model specification to be supplied to the `object` argument, but received a(n) `data.frame` object.
Code
fn(newdata = "boop!")
Condition
Error in `fn()`:
! Please use `new_data` instead of `newdata`.
Code
check_outcome(factor(1:2), reg_spec)
Condition
Error in `check_outcome()`:
! For a regression model, the outcome should be `numeric`, not a `factor`.
Code
check_outcome(NULL, reg_spec)
Condition
Error:
! `linear_reg()` was unable to find an outcome.
i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.
Code
check_outcome(tibble::new_tibble(list(), nrow = 10), reg_spec)
Condition
Error:
! `linear_reg()` was unable to find an outcome.
i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.
Code
fit(reg_spec, ~mpg, mtcars)
Condition
Error:
! `linear_reg()` was unable to find an outcome.
i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.
Code
fit_xy(reg_spec, data.frame(x = 1:5), y = NULL)
Condition
Error:
! `linear_reg()` was unable to find an outcome.
i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.
Code
check_outcome(1:2, class_spec)
Condition
Error in `check_outcome()`:
! For a classification model, the outcome should be a `factor`, not a `integer`.
Code
check_outcome(NULL, class_spec)
Condition
Error:
! `logistic_reg()` was unable to find an outcome.
i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.
Code
check_outcome(tibble::new_tibble(list(), nrow = 10), class_spec)
Condition
Error:
! `logistic_reg()` was unable to find an outcome.
i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.
Code
fit(class_spec, ~mpg, mtcars)
Condition
Error:
! `logistic_reg()` was unable to find an outcome.
i Ensure that you have specified an outcome column and that it hasn't been removed in pre-processing.
Code
check_outcome(1:2, cens_spec)
Condition
Error in `check_outcome()`:
! For a censored regression model, the outcome should be a `Surv` object, not a `integer`.
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