The conversion of categorical variables into a suitable form for neural networks is accomplished through One Hot Encoding. This method creates binary variables for each category, essentially transforming categorical data into a numeric format that neural networks can process effectively.
Neural networks require input features to be in a numerical format. Categorical variables, which represent discrete values or categories (like 'red', 'green', 'blue'), cannot be directly used in most machine learning algorithms without being transformed. One Hot Encoding addresses this by creating a new binary column for each category, where each column is assigned a value of either 0 or 1 depending on whether the observation belongs to that category. For example, if you have a variable "Color" with categories 'Red', 'Green', and 'Blue', One Hot Encoding would create three new columns, one for each color, resulting in an output where each row has a 1 in the column corresponding to its color and 0s in the others.
This approach helps prevent the model from assuming any ordinal relationship among the categories, which is crucial since neural networks interpret numerical input in terms of mathematical relationships. By converting categories into a one-hot representation, all categories maintain equal importance without introducing bias regarding their order or scale.
Other methods like Feature Scaling