Machine learning optimization refers to the process of adjusting the parameters of a machine learning model to minimize or maximize an objective function, typically related to predictive accuracy or error. This involves finding the best set of parameters that will allow the model to generalize well on unseen data, ensuring it performs effectively in real-world applications. It is crucial for improving the performance of algorithms, enabling them to learn from data and make predictions or decisions based on that learning.
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