Function Approximation
Function approximation is a technique used in data science and artificial intelligence to estimate an unknown function based on a set of input-output pairs. The goal is to find a function that can accurately predict the output for any given input. This technique is commonly used in machine learning, where the model is trained on a set of labeled data to learn the underlying patterns and relationships between the input and output variables. Function approximation can be performed using various algorithms such as linear regression, polynomial regression, neural networks, and decision trees. The accuracy of the function approximation depends on the quality and quantity of the training data, the complexity of the model, and the chosen algorithm.
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