Description: Hyperparameters are configurations set before training a machine learning model and are crucial for its performance. These parameters are not learned from the data but must be defined manually, meaning their tuning can significantly impact the model’s ability to generalize and make accurate predictions. The importance of hyperparameters lies in their ability to influence model complexity, convergence speed, and final accuracy. For example, in a machine learning model, hyperparameters may include the learning rate, the number of hidden layers, and the batch size. Improper tuning of these hyperparameters can lead to issues such as overfitting, where the model adapts too closely to the training data and loses its generalization capability. Conversely, optimal tuning can significantly enhance model performance, allowing it to learn relevant patterns in the data. In summary, hyperparameter optimization is an essential process in developing machine learning models, as it largely determines their effectiveness and applicability in real-world tasks.
