What are hyperparameters in machine learning and why are they important?
Hyperparameters are settings that you can adjust before training a model. They control the learning process and can significantly affect model performance.
Can you give me some examples of hyperparameters?
Sure! Examples include learning rate, number of trees in a random forest, and the number of hidden layers in a neural network.
How do I choose the right hyperparameters?
Choosing the right hyperparameters often involves techniques like grid search, random search, or using algorithms like Bayesian optimization.
What happens if I set hyperparameters incorrectly?
Incorrect hyperparameter settings can lead to overfitting, underfitting, or poor model performance, making it crucial to tune them properly.
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