When working with machine learning models in TensorFlow, handling categorical features efficiently is crucial for achieving good performance. TensorFlow provides various tools to manage this process, and one of the most powerful among them is utilizing feature columns to define data transformations. In this article, we will explore how to utilize feature columns to embed categorical features, which is an essential technique for preparing your data set for deep learning models.
Understanding Categorical Features
Categorical features or variables are features that represent categories or groups that do not have numerical meaning. These can be nominal (no inherent order) or ordinal (has an order). For instance, colors (red, blue, green) in a dataset are nominal categories, whereas ratings (poor, average, good) can be considered ordinal categories.
The Role of Embedding in Categorical Features
Embedding is a technique of converting categorical data (often represented as sparse, one-hot-encoded vectors) into dense vectors of fixed size. This is particularly useful in neural networks because dense vectors allow the representations of categories to take advantage of the multi-dimensional geometry of the space, facilitating better performance of machine learning models.
Working with Feature Columns
TensorFlow's feature columns provide a flexible and organized way to describe how input data should be transformed into the features that can be used by the machine learning models. Let's dive into how to implement embedding of categorical features using feature columns in TensorFlow.
Step-by-Step Guide
Consider a dataset with a categorical feature: "color" which can take the following values: red, blue, and green.
Define Categorical Feature Column
Let’s create a categorical feature column using TensorFlow:
import tensorflow as tf
# Define a categorical feature column
color_feature = tf.feature_column.categorical_column_with_vocabulary_list(
'color', ['red', 'blue', 'green'])Here, we have defined a categorical feature column named color_feature with a predefined vocabulary list of possible categories.
Create an Embedding Column
Create an embedding feature column from the defined categorical feature column:
# Define an embedding column
color_embedding = tf.feature_column.embedding_column(color_feature, dimension=8)The embedding_column() function creates an embedding for our categorical feature where dimension=8 indicates the size of the embedding space (the length of the dense vector representing each category).
Use Feature Columns in a Neural Network Model
These feature columns can now be used to input tensors to a neural network model. Below is an example using a TensorFlow Estimator:
# Building a deep neural network
estimator = tf.estimator.DNNClassifier(
hidden_units=[128, 64],
feature_columns=[color_embedding],
n_classes=3)
# Example to show how it could be trained
# estimator.train(input_fn=train_input_fn, steps=1000)In this example, embedding_column helps the neural network to train more efficiently with categorical data. This transforms the sparse representation of features into a dense one which is effective for learning better-patterned feature interactions.
Conclusion
Embedding categorical features using TensorFlow feature columns is a powerful approach, greatly improving a model’s performance by offering a compact and weight-efficient way of handling categorical data. This also allows capturing categorical variable relationships and patterns that make better predictions possible. While these transformations can take some time to set up, it is worth experimenting with and may pay dividends in the performance of your predictive models.
