What are some examples of hyperparameter tuning in different algorithms?

What are some examples of hyperparameter tuning in different algorithms?

Hyperparameter tuning is crucial for optimizing machine learning models. For instance, in decision trees, you can tune the maximum depth and minimum samples split. In neural networks, you might adjust the learning rate and batch size.

Can you give me a specific example for a neural network?

Sure! For a neural network, you might start with a learning rate of 0.01 and then use techniques like grid search or random search to find the optimal learning rate, which could be 0.001 or 0.1.

What about support vector machines?

In support vector machines, you can tune parameters like the kernel type (linear, polynomial, RBF) and the regularization parameter C, which controls the trade-off between achieving a low training error and a low testing error.

How do I know which hyperparameters to tune?

Start by understanding the algorithm's documentation and common practices. You can also analyze the model's performance metrics to identify which parameters have the most significant impact.