Model hyperparameters are variables which should be selected before training the given machine learning model. Generally, they are determined manually. Different combinations of hyperparameters for same algorithm and training data lead to different models. They cannot be learned from data.
Examples of hyperparameters for different algorithms:
- Number of decision trees
- Maximum depth of decision trees
- Number of centroids