=================================================================================

Hyperparameter tuning is the process of finding the optimal configuration of hyperparameters for a machine learning model to achieve the best performance. Hyperparameters are external configuration settings that are not learned from the training data but are set before the training process begins. They can significantly impact the performance of a model, and finding the right values for these hyperparameters is crucial for building an accurate and robust model.

Examples of hyperparameters include learning rate, regularization strength, the number of hidden layers and neurons in a neural network, and the depth of a decision tree. The process of hyperparameter tuning often involves trying different combinations of hyperparameter values, training the model with each combination, and evaluating the model's performance to identify the set of hyperparameters that yield the best results.

Common techniques for hyperparameter tuning include grid search, random search, and more advanced methods like Bayesian optimization. The goal is to find the hyperparameter values that optimize the model's performance on a validation set or through cross-validation.

AutoML (Automated Machine Learning) platforms, such as Google Cloud AutoML, are designed to automate the process of applying machine learning models. These platforms can select the best model, tune hyperparameters, and even handle feature selection to some extent.

============================================