In conclusion, Bayesian optimization does not change the internal structure of the model—things like the number of layers, the activation functions, or the gradients. Instead, it focuses on external hyperparameters. These are settings that control how the model behaves during training and how it processes the data, but they are not part of the model’s architecture itself. For instance, in this code, Bayesian optimization adjusts: C (Regularization Parameter): This determines how much the model tries to fit the training data exactly versus how much it tries to keep things simple. A higher C means the model will try harder to match the training data, while a lower C encourages simpler solutions that generalize better to unseen data. Gamma (Kernel Coefficient): This affects how much…
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