Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

The distinction between parametric and non-parametric models primarily revolves around the assumptions made about the underlying data distribution and the flexibility of the models themselves. Parametric models are characterized by a specific set of assumptions about the form of the function representing the data. This means that they typically have a fixed structure, such as a linear equation, which limits their adaptability to varying patterns in the data. As a result, they are generally less flexible compared to non-parametric models.

On the other hand, non-parametric models make fewer assumptions regarding the data structure. They can adapt more freely to the complexity of the data, allowing for greater flexibility in modeling relationships. This allows non-parametric models to capture a wider range of possible forms that the underlying patterns may take.

Understanding this flexibility is crucial; parametric models tend to perform well when the data truly follows the assumed distribution. However, when the actual data deviates from these assumptions, parametric models may not perform as effectively. Non-parametric models can adjust to this variability, making them suitable for more complex datasets where patterns are not easily defined or may vary significantly.

The other options either mischaracterize the nature of non-parametric models or inaccurately define the relationship between parameters and model forms. Non-param