Can you differentiate between parametric and non-parametric models?

Parametric models have a fixed number of parameters and assume a specific form for the underlying data distribution (e.g., linear regression, logistic regression). They are often simpler and require less data to estimate the parameters but might not capture complex patterns if the assumptions are wrong.

Non-parametric models do not assume a fixed form for the data distribution and can adapt to the data’s complexity (e.g., k-nearest neighbors, decision trees). They are more flexible and can capture intricate patterns but may require more data and computational resources.