One of the criteria to classify machine learning or statistical learning approaches is parametric vs non-parametric models. This post presents the contrast between the two.
A machine learning model is simply a mathematical function $Y = f(X)$ between the input (X) and the output ($Y$). The goal of different machine learning approaches is to learn this function given some observed input and output data.
A parametric approach assumes the functional form (i.e., shape) of the mathematical function ($f$) by construction. That is, it assumes that the function belongs to a particular family of mathematical functions, e.g., linear, quadratic etc. The goal is now to determine the coefficients (parameters) of the different components of the function basis the training data. Linear regression is an example of such an approach. It is assumed that the input and output follow the relation $Y = \beta_0 + \beta_1 X$ and the goal is to determine the values of the parameters, i.e., the coefficients $\beta_0$ and $\beta_1$. Note that the function belongs to the family of straight lines and different values of the parameters will form different straight lines.
A non-parametric approach on the other hand does not make any assumptions about the functional form, it is very flexible and can take any shape. It could be a very complex function, combination of extremely large number of non-linear functions or it could be a rule like large margin boundary or it could be be a simple estimation of density or discriminant outcome in the desired input space. k-nearest neighbor and decision trees are non-parametric approaches and there are no inherent assumptions about the functional forms.
The following points present a contrast between the two approaches:
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