3 Ways to Encode Categorical Variables for Deep Learning

Machine learning and deep learning models, like those in Keras, require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an integer encoding and a one hot encoding, although a newer technique called learned embedding may provide a useful middle ground between these two methods. In this tutorial, you will discover how to encode categorical data when developing neural network models in Keras. How to Encode Categorical Data for Deep Learning in Keras Photo by Ken Dixon, some rights reserved. A categorical variable is a variable whose values take on the value of labels.