Intuition behind the Different Feature Columns in TensorFlow | by Megha Natarajan | Nov, 2023

TensorFlow’s feature columns provide a powerful way to handle a variety of data types when building machine learning models. They act as intermediaries between raw data and estimators in TensorFlow, making it easier to process and transform different types of data into formats that machine learning models can understand. Let’s dive into the different types of feature columns available in TensorFlow and how they can be utilized.

The most basic feature column type is the numeric column. It is used for continuous variables that represent numeric values. These columns are used as-is without any need for transformation.

age = tf.feature_column.numeric_column("age")

Bucketized columns are used to transform continuous variables into categorical variables by dividing the variable’s range into different buckets.

age_buckets = tf.feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])

This column is used for categorical features with known values, and is particularly useful when the categorical values are already encoded as integers.

gender = tf.feature_column.categorical_column_with_identity('gender', num_buckets=2)

When you have a categorical feature with a known set of values, the vocabulary list column can be used. It maps string values to integer indices.

education = tf.feature_column.categorical_column_with_vocabulary_list('education', ['Bachelors', 'Masters', 'PhD'])

For categorical features with numerous values, the hashed feature column can be used. It uses a hash function to map values to integer indices.

profession = tf.feature_column.categorical_column_with_hash_bucket('profession', hash_bucket_size=1000)

Crossed feature columns combine multiple features into a single feature, which is useful for capturing interactions between different features.

crossed_feature = tf.feature_column.crossed_column([age_buckets, education], hash_bucket_size=1000)

Embedding columns are used to convert high-dimensional categorical features into a lower-dimensional space. This is especially useful for handling sparse data efficiently.

embedded_profession = tf.feature_column.embedding_column(profession, dimension=8)

Indicator columns are used to convert categorical features into a one-hot encoded numeric array. This is simpler than embedding columns, but can be less efficient for high-cardinality features.

education_one_hot = tf.feature_column.indicator_column(education)

TensorFlow’s feature columns offer versatile ways to handle different types of data, making them indispensable tools in the machine learning pipeline. Whether dealing with numerical, categorical, or even complex combinations of features, TensorFlow’s feature columns provide an efficient and streamlined approach to preprocess and feed data into your models. Understanding and utilizing these columns effectively can significantly enhance the performance and accuracy of your machine learning models.