When to Use One-Hot Encoding in Deep Learning?


Preprocessing data is an essential step before building a Deep Learning model. When creating a deep learning project, it is not always that we come across clean and well-formatted data.  Therefore while doing any operation with the data, it is mandatory to clean it and put it in a formatted way. Data preprocessing is the process of preparing the raw data and making it suitable for a machine or deep learning model and it is also the first and crucial step while creating a model. Using the new revolutionary technologies such as Artificial Intelligence and Deep Learning for smart decision making and driving business growth but without applying the right data processing techniques, it is of no real use.

Several machine learning algorithms as well as Deep Learning Algorithms are generally unable to work with categorical data when fed directly into the model. These categories must be further converted into numbers and the same is required for both the input and output variables in the data that are categorical. If you are in the field of data science, you must have probably heard about the term “One-hot Encoding”. The Sklearn documentation defines it as “to encode categorical integer features using a one-hot scheme”. But what is it exactly?

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What is One Hot Encoding?

As a machine can only understand numbers and cannot understand the text in the first place, this essentially becomes the case with Deep Learning & Machine Learning algorithms. One hot encoding can be defined as the essential process of converting the categorical data variables to be provided to machine and deep learning algorithms which in turn improve predictions as well as classification accuracy of a model. One Hot Encoding is a common way of preprocessing categorical features for machine learning models. This type of encoding creates a new binary feature for each possible category and assigns a value of 1 to the feature of each sample that corresponds to its original category.

One hot encoding is a highly essential part of the feature engineering process in training for learning techniques. For example, we had our variables like colors and the labels were “red,” “green,” and “blue,” we could encode each of these labels as a three-element binary vector as Red: [1, 0, 0], Green: [0, 1, 0], Blue: [0, 0, 1]. The Categorical data while processing, must be converted to a numerical form. One-hot encoding is generally applied to the integer representation of the data. Here the integer encoded variable is removed and a new binary variable is added for each unique integer value. During the process, it takes a column that has categorical data, which has been label encoded and then splits the following column into multiple columns. The numbers are replaced by 1s and 0s randomly, depending on which column has what value. While the method is helpful for some ordinal situations, some input data does not have any ranking for category values, and this can lead to issues with predictions and poor performance.

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