This function is similar to `make_class_pred()`

, but is useful when you have
a large number of class probability columns and want to use `tidyselect`

helpers. It appends the new `class_pred`

vector as a column on the original
data frame.

## Usage

```
append_class_pred(
.data,
...,
levels,
ordered = FALSE,
min_prob = 1/length(levels),
name = ".class_pred"
)
```

## Arguments

- .data
A data frame or tibble.

- ...
One or more unquoted expressions separated by commas to capture the columns of

`.data`

containing the class probabilities. You can treat variable names like they are positions, so you can use expressions like`x:y`

to select ranges of variables or use selector functions to choose which columns. For`make_class_pred`

, the columns for all class probabilities should be selected (in the same order as the`levels`

object). For`two_class_pred`

, a vector of class probabilities should be selected.- levels
A character vector of class levels. The length should be the same as the number of selections made through

`...`

, or length`2`

for`make_two_class_pred()`

.- ordered
A single logical to determine if the levels should be regarded as ordered (in the order given). This results in a

`class_pred`

object that is flagged as ordered.- min_prob
A single numeric value. If any probabilities are less than this value (by row), the row is marked as

*equivocal*.- name
A single character value for the name of the appended

`class_pred`

column.

## Examples

```
# The following two examples are equivalent and demonstrate
# the helper, append_class_pred()
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
species_probs %>%
mutate(
.class_pred = make_class_pred(
.pred_bobcat, .pred_coyote, .pred_gray_fox,
levels = levels(Species),
min_prob = .5
)
)
#> # A tibble: 110 × 5
#> Species .pred_bobcat .pred_coyote .pred_gray_fox .class_pred
#> <fct> <dbl> <dbl> <dbl> <clss_prd>
#> 1 gray_fox 0.0976 0.0530 0.849 gray_fox
#> 2 gray_fox 0.155 0.139 0.706 gray_fox
#> 3 bobcat 0.501 0.0880 0.411 bobcat
#> 4 gray_fox 0.256 0 0.744 gray_fox
#> 5 gray_fox 0.463 0.287 0.250 [EQ]
#> 6 bobcat 0.811 0 0.189 bobcat
#> 7 bobcat 0.911 0.0888 0 bobcat
#> 8 bobcat 0.898 0.0517 0.0500 bobcat
#> 9 bobcat 0.771 0.229 0 bobcat
#> 10 bobcat 0.623 0.325 0.0517 bobcat
#> # ℹ 100 more rows
lvls <- levels(species_probs$Species)
append_class_pred(
.data = species_probs,
contains(".pred_"),
levels = lvls,
min_prob = .5
)
#> # A tibble: 110 × 5
#> Species .pred_bobcat .pred_coyote .pred_gray_fox .class_pred
#> <fct> <dbl> <dbl> <dbl> <clss_prd>
#> 1 gray_fox 0.0976 0.0530 0.849 gray_fox
#> 2 gray_fox 0.155 0.139 0.706 gray_fox
#> 3 bobcat 0.501 0.0880 0.411 bobcat
#> 4 gray_fox 0.256 0 0.744 gray_fox
#> 5 gray_fox 0.463 0.287 0.250 [EQ]
#> 6 bobcat 0.811 0 0.189 bobcat
#> 7 bobcat 0.911 0.0888 0 bobcat
#> 8 bobcat 0.898 0.0517 0.0500 bobcat
#> 9 bobcat 0.771 0.229 0 bobcat
#> 10 bobcat 0.623 0.325 0.0517 bobcat
#> # ℹ 100 more rows
```