Hyperparameter Optimization

Hyperparameter optimization is the process of fine-tuning the settings of a machine learning model—such as learning rate or number of layers—to improve its performance. These settings are not learned from data but set beforehand. Optimizing them involves trying different combinations to find the best setup that makes the model more accurate or efficient. This process helps ensure the model generalizes well to new data and works effectively for its specific task.