fit.FeatureSpec {tfdatasets} | R Documentation |
Fits a feature specification.
Description
This function will fit
the specification. Depending
on the steps added to the specification it will compute
for example, the levels of categorical features, normalization
constants, etc.
Usage
## S3 method for class 'FeatureSpec'
fit(object, dataset = NULL, ...)
Arguments
object |
A feature specification created with |
dataset |
(Optional) A TensorFlow dataset. If |
... |
(unused) |
Value
a fitted FeatureSpec
object.
See Also
-
feature_spec()
to initialize the feature specification. -
dataset_use_spec()
to create a tensorflow dataset prepared to modeling. -
steps to a list of all implemented steps.
Other Feature Spec Functions:
dataset_use_spec()
,
feature_spec()
,
step_bucketized_column()
,
step_categorical_column_with_hash_bucket()
,
step_categorical_column_with_identity()
,
step_categorical_column_with_vocabulary_file()
,
step_categorical_column_with_vocabulary_list()
,
step_crossed_column()
,
step_embedding_column()
,
step_indicator_column()
,
step_numeric_column()
,
step_remove_column()
,
step_shared_embeddings_column()
,
steps
Examples
## Not run:
library(tfdatasets)
data(hearts)
hearts <- tensor_slices_dataset(hearts) %>% dataset_batch(32)
# use the formula interface
spec <- feature_spec(hearts, target ~ age) %>%
step_numeric_column(age)
spec_fit <- fit(spec)
spec_fit
## End(Not run)