Compare grid search, random search, and Bayesian optimization for hyperparameter tuning. When would you use each approach, and how do you avoid overfitting during tuning?

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Hyperparameter tuning is the process of finding optimal configuration settings for a machine learning model. Unlike model parameters that are learned during training, hyperparameters are set before training and control aspects like model complexity, learning rate, and…