Description: Hyperparameter tuning is a method used to explore the hyperparameter space in machine learning models. Hyperparameters are configurations set before model training that can significantly influence performance. Hyperparameter tuning allows for identifying the optimal combination of these hyperparameters, which can enhance the model’s accuracy and effectiveness. This process involves systematically evaluating different configurations, using performance metrics to determine which is the most effective. There are several approaches to carry out hyperparameter tuning, including random search, grid search, and more advanced methods like Bayesian optimization. Each of these methods has its own characteristics and advantages, allowing researchers and developers to select the one that best fits their needs and resources. Hyperparameter tuning is essential in developing robust and reliable models, as inadequate tuning can lead to overfitting or suboptimal model performance on unseen data.
