Model Tuning
Model tuning is the process of adjusting the hyperparameters of a machine learning model to optimize its performance on a given task. Hyperparameters are parameters that are not learned from data, but rather set by the user before training the model. Examples of hyperparameters include learning rate, regularization strength, and number of hidden layers in a neural network. Model tuning involves selecting the best combination of hyperparameters that results in the highest accuracy or lowest error rate on a validation set. This process can be done manually by trying different combinations of hyperparameters, or automatically using techniques such as grid search, random search, or Bayesian optimization.
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