👉 Label math, also known as label-based or label-matching methods, is a fundamental approach in machine learning, especially for supervised learning tasks. It involves training a model to predict discrete labels or categories directly from input data, often without explicit feature engineering. The model learns to map inputs to a set of predefined labels by minimizing the difference between its predicted probabilities and the actual labels. This is typically done using loss functions like cross-entropy loss, which penalize incorrect predictions more heavily. Label math is particularly useful when dealing with categorical data and can be applied to tasks like image classification, text categorization, and more. Despite its simplicity, it forms the basis for many advanced models and techniques in deep learning and traditional machine learning.
