A hyperparameter is a setting chosen before training, such as a tree's maximum depth, the number of neighbors, or the regularization strength. It differs from parameters, which are learned from the data. They are selected through validation rather than on the test data.
Example
RandomForestClassifier(n_estimators=200, max_depth=8)