Defining a cross_column for dependent categorical features
Defining a cross_column for dependent categorical features
My features include two columns:
department
service
Because department and service are mostly "sparse" columns, they were defined as:
department
service
department_column = tf.feature_column.categorical_column_with_hash_bucket("department", 200, dtype=tf.int32)
feature_dict["department"] = tf.feature_column.indicator_column(department_column)
service_column = tf.feature_column.categorical_column_with_hash_bucket("service", 4000, dtype=tf.int32)
feature_dict["service"] = tf.feature_column.indicator_column(service_column)
But when we define this features as independent columns we're not describing the relation between this columns: service only makes sense only in the scope of particular department.
service
department
To describe the relation, it seems a good idea to use crossed columns.
The solution consist of two stages:
crossed_column
crossed_column
embedding_column
indicator_column
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