What are hyperparameters and why are they important in machine learning?
Hyperparameters are settings that you configure before training a model. They control the learning process and can significantly affect model performance.
Can you give me some examples of hyperparameters?
Sure! Common examples include learning rate, batch size, number of epochs, and the architecture of the model itself.
How do I choose the right hyperparameters?
Choosing the right hyperparameters often involves experimentation. Techniques like grid search, random search, or using algorithms like Bayesian optimization can help.
What happens if I set hyperparameters incorrectly?
Incorrect hyperparameter settings can lead to underfitting or overfitting, resulting in poor model performance.
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