step_classdist() creates a specification of a recipe step that will
convert numeric data into Mahalanobis distance measurements to the data
centroid. This is done for each value of a categorical class variable.
Usage
step_classdist(
recipe,
...,
class,
role = "predictor",
trained = FALSE,
mean_func = mean,
cov_func = cov,
pool = FALSE,
log = TRUE,
objects = NULL,
prefix = "classdist_",
keep_original_cols = TRUE,
skip = FALSE,
id = rand_id("classdist")
)Arguments
- recipe
A recipe object. The step will be added to the sequence of operations for this recipe.
- ...
One or more selector functions to choose variables for this step. See
selections()for more details.- class
A single character string that specifies a single categorical variable to be used as the class.
- role
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
- trained
A logical to indicate if the quantities for preprocessing have been estimated.
- mean_func
A function to compute the center of the distribution.
- cov_func
A function that computes the covariance matrix
- pool
A logical: should the covariance matrix be computed by pooling the data for all of the classes?
- log
A logical: should the distances be transformed by the natural log function?
- objects
Statistics are stored here once this step has been trained by
prep().- prefix
A character string for the prefix of the resulting new variables. See notes below.
- keep_original_cols
A logical to keep the original variables in the output. Defaults to
TRUE.- skip
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked whenprep()is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when usingskip = TRUEas it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe with the new step added to the
sequence of any existing operations.
Details
step_classdist will create a new column for every unique value of
the class variable. The resulting variables will not replace the original
values and, by default, have the prefix classdist_. The naming format can
be changed using the prefix argument.
Class-specific centroids are the multivariate averages of each predictor using the data from each class in the training set. When pre-processing a new data point, this step computes the distance from the new point to each of the class centroids. These distance features can be very effective at capturing linear class boundaries. For this reason, they can be useful to add to an existing predictor set used within a nonlinear model. If the true boundary is actually linear, the model will have an easier time learning the training data patterns.
Note that, by default, the default covariance function requires
that each class should have at least as many rows as variables
listed in the terms argument. If pool = TRUE,
there must be at least as many data points are variables
overall.
Tidying
When you tidy() this step, a tibble is returned with
columns terms, value, class , and id:
- terms
character, the selectors or variables selected
- value
numeric, location of centroid
- class
character, name of the class
- id
character, id of this step
Case weights
This step performs an supervised operation that can utilize case weights.
As a result, case weights are used with frequency weights as well as
importance weights. For more information,, see the documentation in
case_weights and the examples on tidymodels.org.
See also
Other multivariate transformation steps:
step_classdist_shrunken(),
step_depth(),
step_geodist(),
step_ica(),
step_isomap(),
step_kpca(),
step_kpca_poly(),
step_kpca_rbf(),
step_mutate_at(),
step_nnmf(),
step_nnmf_sparse(),
step_pca(),
step_pls(),
step_ratio(),
step_spatialsign()
Examples
data(penguins, package = "modeldata")
penguins <- penguins[complete.cases(penguins), ]
penguins$island <- NULL
penguins$sex <- NULL
# in case of missing data...
mean2 <- function(x) mean(x, na.rm = TRUE)
# define naming convention
rec <- recipe(species ~ ., data = penguins) %>%
step_classdist(all_numeric_predictors(),
class = "species",
pool = FALSE, mean_func = mean2, prefix = "centroid_"
)
# default naming
rec <- recipe(species ~ ., data = penguins) %>%
step_classdist(all_numeric_predictors(),
class = "species",
pool = FALSE, mean_func = mean2
)
rec_dists <- prep(rec, training = penguins)
dists_to_species <- bake(rec_dists, new_data = penguins)
## on log scale:
dist_cols <- grep("classdist", names(dists_to_species), value = TRUE)
dists_to_species[, c("species", dist_cols)]
#> # A tibble: 333 × 4
#> species classdist_Adelie classdist_Chinstrap classdist_Gentoo
#> <fct> <dbl> <dbl> <dbl>
#> 1 Adelie 1.04 3.19 5.10
#> 2 Adelie 0.670 2.61 4.61
#> 3 Adelie 1.45 2.39 4.68
#> 4 Adelie 1.20 3.42 5.08
#> 5 Adelie 1.72 3.57 5.35
#> 6 Adelie 0.903 2.87 4.90
#> 7 Adelie 1.78 3.61 4.89
#> 8 Adelie 1.66 2.17 4.88
#> 9 Adelie 2.05 3.85 5.45
#> 10 Adelie 2.72 4.24 5.35
#> # ℹ 323 more rows
tidy(rec, number = 1)
#> # A tibble: 1 × 4
#> terms value class id
#> <chr> <dbl> <chr> <chr>
#> 1 all_numeric_predictors() NA NA classdist_s3Z4q
tidy(rec_dists, number = 1)
#> # A tibble: 12 × 4
#> terms value class id
#> <chr> <dbl> <chr> <chr>
#> 1 bill_length_mm 38.8 Adelie classdist_s3Z4q
#> 2 bill_depth_mm 18.3 Adelie classdist_s3Z4q
#> 3 flipper_length_mm 190. Adelie classdist_s3Z4q
#> 4 body_mass_g 3706. Adelie classdist_s3Z4q
#> 5 bill_length_mm 48.8 Chinstrap classdist_s3Z4q
#> 6 bill_depth_mm 18.4 Chinstrap classdist_s3Z4q
#> 7 flipper_length_mm 196. Chinstrap classdist_s3Z4q
#> 8 body_mass_g 3733. Chinstrap classdist_s3Z4q
#> 9 bill_length_mm 47.6 Gentoo classdist_s3Z4q
#> 10 bill_depth_mm 15.0 Gentoo classdist_s3Z4q
#> 11 flipper_length_mm 217. Gentoo classdist_s3Z4q
#> 12 body_mass_g 5092. Gentoo classdist_s3Z4q