Sure! In machine learning, parametric and non-parametric models are two different approaches to building models.

Parametric models make assumptions about the underlying distribution of the data. These models have a fixed number of parameters, which are estimated from the training data. Examples of parametric models include linear regression, logistic regression, and Naive Bayes. Parametric models are often computationally efficient and require less data to train. However, they may not be flexible enough to capture complex patterns in the data if the assumptions made about the distribution are not accurate.

Non-parametric models, on the other hand, do not make strong assumptions about the underlying distribution of the data. These models are more flexible and can capture complex patterns in the data. Examples of non-parametric models include decision trees, random forests, and support vector machines. Non-parametric models can handle a wide range of data distributions and are often more accurate in capturing complex relationships. However, they may require more data to train and can be computationally expensive.

In summary, parametric models make assumptions about the data distribution and have a fixed number of parameters, while non-parametric models do not make strong assumptions and are more flexible in capturing complex patterns.

I hope this explanation helps! Let me know if you have any further questions.