Unified prediction interface for both supervised and unsupervised models
Usage
# S3 method for class 'tidylearn_model'
predict(object, new_data = NULL, type = "response", ...)Arguments
- object
A tidylearn model object
- new_data
A data frame containing the new data. If NULL, uses training data.
- type
Type of prediction, for supervised models only:
"response"(default),"prob"or"class". Note that"response"is method-dependent – logistic regression returns probabilities, trees and forests return class labels – so pass"class"explicitly when you want labels. Ignored by unsupervised models, whose output is determined by the method.- ...
Additional arguments
Value
For supervised models, a tibble with a
.pred column; with type = "prob", one column per class
instead. For unsupervised models, the method's natural output: an
.obs_id column plus component scores for "pca" and
"mds", or plus a cluster column for the clustering
methods.
Unsupervised models differ in whether they can handle new data.
"pca" projects it and "kmeans" assigns it to the
nearest centre; "pam", "clara", "dbscan",
"mds" and "hclust" have no out-of-sample projection
and error if new_data is supplied. For hierarchical
clustering, cut the tree with tidy_cutree() instead.
Examples
# \donttest{
model <- tl_model(mtcars, mpg ~ wt + hp, method = "linear")
predict(model)
#> # A tibble: 32 × 1
#> .pred
#> <dbl>
#> 1 23.6
#> 2 22.6
#> 3 25.3
#> 4 21.3
#> 5 18.3
#> 6 20.5
#> 7 15.6
#> 8 22.9
#> 9 22.0
#> 10 20.0
#> # ℹ 22 more rows
predict(model, new_data = mtcars[1:5, ])
#> # A tibble: 5 × 1
#> .pred
#> <dbl>
#> 1 23.6
#> 2 22.6
#> 3 25.3
#> 4 21.3
#> 5 18.3
# }