Approximation in Machine Learning

Approximation in machine learning refers to the process of creating a model that predicts outcomes based on available data. Since it's often impossible to capture every detail of a complex situation, these models aim to find patterns and relationships that closely mimic reality, even if they're not perfect. Just as a good map represents the terrain without showing every rock and tree, machine learning models simplify real-world data to make informed predictions, balancing accuracy and computational efficiency. This ability to generalize from limited information is crucial for making decisions in various applications, from healthcare to finance.