Nothing
Code
discretize(letters)
Condition
Error in `discretize()`:
x Only numeric `x` is accepted.
i The `x` was passed a character vector.
Code
discretize(1:100)
Output
Bins: 5 (includes missing category)
Breaks: -Inf, 25.75, 50.5, 75.25, Inf
Code
discretize(1:100, cuts = 6)
Output
Bins: 7 (includes missing category)
Code
discretize(1:100, keep_na = FALSE)
Output
Bins: 4
Breaks: -Inf, 25.75, 50.5, 75.25, Inf
Code
res <- discretize(1:2)
Condition
Warning:
Data not binned; too few unique values per bin. Adjust `min_unique` as needed.
Code
res
Output
Too few unique data points. No binning was used.
Code
recipe(~., data = example_data) %>% step_discretize(x1, x2, options = list(
prefix = "hello")) %>% prep()
Condition
Warning:
Note that the options `prefix` and `labels` will be applied to all variables.
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 2
-- Training information
Training data contained 1000 data points and no incomplete rows.
-- Operations
* Discretize numeric variables from: x1 and x2 | Trained
Code
recipe(~., data = ex_tr) %>% step_discretize(x1, num_breaks = 1) %>% prep()
Condition
Error in `step_discretize()`:
Caused by error in `recipes::discretize()`:
! There should be at least 2 `cuts` but 1 was supplied.
Code
recipe(~., data = ex_tr) %>% step_discretize(x1, num_breaks = 100) %>% prep()
Condition
Warning:
Data not binned; too few unique values per bin. Adjust `min_unique` as needed.
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 3
-- Training information
Training data contained 100 data points and no incomplete rows.
-- Operations
* Discretize numeric variables from: x1 | Trained
Code
recipe(~., data = ex_tr) %>% step_discretize(x1, options = list(prefix = "@$")) %>%
prep()
Condition
Warning:
The prefix "@$" is not a valid R name. It has been changed to "X..".
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 3
-- Training information
Training data contained 100 data points and no incomplete rows.
-- Operations
* Discretize numeric variables from: x1 | Trained
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Operations
* Discretize numeric variables from: <none>
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Training information
Training data contained 32 data points and no incomplete rows.
-- Operations
* Discretize numeric variables from: <none> | Trained
Code
print(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 3
-- Operations
* Discretize numeric variables from: x1
Code
prep(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 3
-- Training information
Training data contained 100 data points and no incomplete rows.
-- Operations
* Discretize numeric variables from: x1 | Trained
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