| Parametric models fix a NUMBER of parameters to build the model in machine learning | The Non-parametric models use a flexible number of parameters to build the model. |
| Parametric analysis is to test group means. | A non-parametric analysis is to test medians. |
| It is applicable only for variables. | It is applicable for both – Variable and Attribute. |
| Parametric methods can be useful in a variety of scenarios, but they function best when the DISPERSION of each group is varied. | Similarly, Non-Parametric Methods can perform well in a variety of CONDITIONS, but their performance is at its best (TOP) when each group's spread is equal. |
| Parametric models hold strong assumptions about the data. | It holds fewer assumptions about data. |
| Examples: Naive Bayes model, logical REGRESSION, etc. | Example: KNN. |
