IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Parametric models differ from non-parametric models primarily in the assumptions they make about the underlying data distribution. Specifically, parametric models assume a specific functional form or distribution for the data, which means they require the data to fit a predefined mathematical structure, such as linear regression assuming a linear relationship between variables or a specific probability distribution like the normal distribution. This allows parametric models to be computationally efficient and require fewer data points, but they may fail to capture the complexities of the data if the assumption does not hold true.

In contrast, non-parametric models do not make strict assumptions about the data's distribution, allowing them to be more flexible and adaptable to a wide variety of data patterns without being tied to a predetermined form. However, this flexibility can sometimes lead to overfitting, especially if there is limited training data.

Recognizing the nature of these models is crucial for data scientists, as the choice between parametric and non-parametric approaches can significantly affect modeling outcomes based on the dataset and the analytical goals.