Description: Label encoding is a fundamental process in the field of machine learning, especially in the context of preprocessing categorical data. This process involves converting categorical labels, which are textual or symbolic representations of classes, into a numerical format that can be interpreted by machine learning algorithms. Neural networks, being mathematical models, require that input and output data be numerical in order to perform calculations and optimizations. Label encoding allows classes to be represented as integers, thereby facilitating model training. There are different encoding methods, such as ordinal encoding, which assigns a unique number to each category, and one-hot encoding, which creates a binary vector for each class. The latter is particularly useful in multi-class classification problems, where each label is converted into a vector that has a value of 1 in the position corresponding to the class and 0 in all others. Proper label encoding is crucial for model performance, as it influences the algorithm’s ability to learn patterns and make accurate predictions.
