Description: Complexity adjustment is a fundamental process in the field of machine learning (AutoML) that focuses on managing the complexity of a model to prevent overfitting. Overfitting occurs when a model fits too closely to the training data, capturing noise and irrelevant patterns instead of generalizing adequately to new data. This phenomenon can lead to poor performance in predicting unseen data. Complexity adjustment involves selecting a model that is flexible enough to capture the underlying relationships in the data but not so complex that it fits the peculiarities of the training data. Common techniques to achieve this balance include regularization, which penalizes model complexity, and cross-validation, which helps evaluate model performance on different subsets of data. Additionally, hyperparameter tuning is a key practice in this process, where specific model parameters are optimized to improve generalization capability. In summary, complexity adjustment is essential for building robust and effective models in machine learning, ensuring they can make accurate predictions on unseen data.
