AI in Dentistry Practice Test 2025 – Comprehensive Exam Prep

Regularization is an essential technique used in machine learning, including applications in dentistry, to prevent overfitting. During model training, overfitting occurs when a model learns the noise and details in the training data to the point that it negatively impacts the model's performance on new, unseen data. Regularization addresses this issue by adding a penalty term to the loss function that the model tries to minimize. This penalty discourages the model from becoming overly complex by limiting the magnitude of the model parameters. As a result, it helps improve the model's generalization capabilities, meaning it can perform better when exposed to new data.

The regularization process shifts the focus from achieving a perfect fit on the training data towards finding a balance between fitting the data well while keeping the model simple. This counteracts the tendency for more complex models to memorize the training data instead of learning the underlying patterns.

The other options do not accurately reflect the primary purpose of regularization. Enhancing the complexity of the model is contrary to the intention of regularization, which seeks to simplify it. Speeding up the training process is not a direct outcome of regularization; in fact, it may add some computational overhead. Lastly, regularization does not pertain to data cleaning, which