# input checking
Code
recipe(~., data = df) %>% step_relu(val1, shift = TRUE) %>% prep(df, verbose = FALSE)
Condition
Error in `step_relu()`:
! Shift argument must be a numeric value.
---
Code
recipe(~., data = df) %>% step_relu(val1, reverse = 3) %>% prep(df, verbose = FALSE)
Condition
Error in `step_relu()`:
! Reverse argument must be a logical value.
---
Code
recipe(~., data = df) %>% step_relu(val1, smooth = "cat") %>% prep(df, verbose = FALSE)
Condition
Error in `step_relu()`:
! Smooth argument must be logical value.
---
Code
recipe(~., data = df) %>% step_relu(val2) %>% prep(df, verbose = FALSE)
Condition
Error in `step_relu()`:
Caused by error in `prep()`:
! All columns selected for the step should be double, or integer.
# prints something
Code
print(rec)
Output
Recipe
Inputs:
role #variables
predictor 2
Operations:
Adding relu transform for val1
---
Code
prep(rec)
Output
Recipe
Inputs:
role #variables
predictor 2
Training data contained 21 data points and no missing data.
Operations:
Adding relu transform for val1 [trained]
# empty printing
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Operations:
Adding relu transform for <none>
---
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Training data contained 32 data points and no missing data.
Operations:
Adding relu transform for <none> [trained]
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