What are hyperparameters and how do you tune them?

Hyperparameters are the configurations that are set before the learning process begins. These include parameters such as learning rate, the number of epochs, and the depth of a decision tree. Unlike model parameters, which are learned during training, hyperparameters must be chosen prior to training. Tuning hyperparameters can be achieved through methods such as grid search, random search, or more advanced techniques like Bayesian optimization. The goal is to find the combination that yields the best model performance on validation data, which helps avoid overfitting and ensures good generalization to unseen data.