Entity Embeddings of Categorical Variables in Neural Networks
NEED FOR ENTITY EMBEDDINGS…. Neural networks has revolutionized computer vision, speech recognition, and natural processing and have replaced or are replacing the long dominating methods. But unlike in the fields above where the data is unstructured, neural networks are not as prominent when dealing with machine learning problems with structured data. In principle a neural network can approximate any continuous function and piece wise continuous functions. However, it is not suitable to approximate arbitrary non-continuous functions as it assumes certain level of continuity in its general form. Interestingly the problems we usually face in nature are often continuous if we use the right representation of data. But unlike unstructured data found in nature, structured data with categorical features may not have continuity at all and even if it has it may not be so obvious. Therefore, natively applying neural networks on structured data with integer representation for categoric