Data preprocessing is a fundamental step before building a deep learning model. When creating a deep learning project, we don’t always find 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 is also the first and decisive step in creating a model. Using the new revolutionary technologies such as Artificial Intelligence and deep learning for intelligent decision making and business growth, but without the right computing techniques it is useless.
Various machine learning algorithms as well as deep learning algorithms usually cannot work with categorical data if this is entered directly into the model. These categories need to be converted more to numbers, and so must the input and output variables in the categorical data. If you work in data science, you have probably heard of the term “onehot encoding”. The Sklearn documentation defines it as “encoding categorical integer features with a one-hot scheme”. But what is it exactly?
What is One Hot Encoding?
Since a machine can only understand numbers and no text at all, this is essentially the case with deep learning and 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 the prediction and classification accuracy of a model. One hot encoding is a common method 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 for the characteristic of each sample that corresponds to its original category.
One hot encoding is a very essential part of the function engineering process in skills learning training. For example, we had our variables as colors and the labels were “red”, “green” and “blue”, we could code each of these labels as a three-element binary vector as red: [1, 0, 0], green: [0, 1, 0], blue: [0, 0, 1]. Categorical data must be converted to numbers during processing. One hot encoding generally applies to the entire 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 contains categorical data encoded in the label, and then splits the next column into multiple columns, replacing the numbers with 1s and 0s at random, depending on which column has which value. While the method is useful for some ordinal situations, some input data does not rank for category values, which can cause problems with predictions and poor performance.
The sample document by SK-Learn defines the process as :
“The input to the encoding transformer should be an array of integers or strings, denoting the values taken on by categorical i.e discrete features. The features are then encoded using a one-hot aka ‘one-of-K’ or ‘dummy’ encoding scheme. This creates a binary column for each category and returns a sparse matrix or dense array, depending on the sparse parameter specified”
The One Hot Encoding technique creates a number of additional features based on the number of unique values in the categorical feature. Every unique value in the category is added as a feature. Hence the One Hot Encoding is known as the process of creating dummy variables. This technique helps create better classifiers and is very effective when combined in a Deep Learning classification model.
An Example of One Hot Encoding in Deep Learning
Suppose we have data with categorical variables, and we want to perform binary classification for the same using a Deep Learning model, therefore to feed the model with data that enables it to make a classification decision, we would require to perform One Hot Encoding during data processing. Here we need to determine whether or not a customer will continue with the bank or not, which makes it a binary classification problem, to be either 0 for no or 1 for yes as our output from the model.
