For DNN model, indicator_column can be used to wrap any
categorical_column_* (e.g., to feed to DNN). Consider to Use
embedding_column if the number of buckets/unique(values) are large.
For Wide (aka linear) model, indicator_column is the internal
representation for categorical column when passing categorical column
directly (as any element in feature_columns) to linear_model. See
linear_model for details.
name = indicator_column(categorical_column_with_vocabulary_list(
'name', ['bob', 'george', 'wanda']))
columns = [name, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
dense_tensor == [[1, 0, 0]] # If "name" bytes_list is ["bob"]
dense_tensor == [[1, 0, 1]] # If "name" bytes_list is ["bob", "wanda"]
dense_tensor == [[2, 0, 0]] # If "name" bytes_list is ["bob", "bob"]
Args
categorical_column
A CategoricalColumn which is created by
categorical_column_with_* or crossed_column functions.
Returns
An IndicatorColumn.
Raises
ValueError
If categorical_column is not CategoricalColumn type.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]