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
# S3 method for cluster_fit predict(object, new_data, type = NULL, opts = list(), ...) # S3 method for cluster_fit predict_raw(object, new_data, opts = list(), ...)
An object of class
A rectangular data object, such as a data frame.
A single character value or
NULL. Possible values are "cluster", or "raw". When
predict()will choose an appropriate value based on the model's mode.
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
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 the tibble will have a
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
function will return the same structure as above but filled with missing values. This does not currently work for multivariate models.
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.
kmeans_spec <- k_means(num_clusters = 5) %>% set_engine("stats") kmeans_fit <- fit(kmeans_spec, ~., mtcars) kmeans_fit %>% predict(new_data = mtcars) #> # A tibble: 32 × 1 #> .pred_cluster #> <fct> #> 1 Cluster_1 #> 2 Cluster_1 #> 3 Cluster_2 #> 4 Cluster_3 #> 5 Cluster_4 #> 6 Cluster_1 #> 7 Cluster_4 #> 8 Cluster_2 #> 9 Cluster_2 #> 10 Cluster_1 #> # … with 22 more rows