predict.cluster_fit: Model predictions

In kbodwin/celery: What the Package Does (One Line, Title Case)

Model predictions

Description

Apply a model to create different types of predictions. predict() can be used for all types of models and uses the "type" argument for more specificity.

Usage

## S3 method for class 'cluster_fit'
predict(object, new_data, type = NULL, opts = list(), ...)

Arguments

object	An object of class cluster_fit
new_data	A rectangular data object, such as a data frame.
type	A single character value or NULL. Possible values are "cluster", or "raw". When NULL, predict() will choose an appropriate value based on the model's mode.
opts	A list of optional arguments to the underlying predict function that will be used when type = "raw". The list should not include options for the model object or the new data being predicted.
...	Arguments to the underlying model's prediction function cannot be passed here (see opts).

Details

If "type" is not supplied to predict(), then a choice is made:

• type = "cluster" for clustering models

predict() is designed to provide a tidy result (see "Value" section below) in a tibble output format.

Value

With the exception of type = "raw", the results of predict.cluster_fit() will be a tibble as many rows in the output as there are rows in new_data and the column names will be predictable.

For clustering results with a single outcome, the tibble will have a .pred_cluster.

Using type = "raw" with predict.cluster_fit() will return the unadulterated results of the prediction function.

When the model fit failed and the error was captured, the predict() function will return the same structure as above but filled with missing values. This does not currently work for multivariate models.

Examples

kmeans_spec <- k_means(k = 5) %>%
  set_engine_celery("stats")

kmeans_fit <- fit(kmeans_spec, ~., mtcars)

kmeans_fit %>%
  predict(new_data = mtcars)

kbodwin/celery documentation built on March 26, 2022, 12:33 a.m.