What is the defining feature of parametric algorithms?

The defining feature of parametric algorithms is that they generate a fixed number of model parameters. This characteristic means that regardless of the size of the dataset, the model will always be defined by a limited number of parameters, which remain unchanged as more data is introduced. This allows for simpler and faster computations, as well as requiring less memory to store the model.

In the context of parametric models, the fixed parameters typically represent the underlying assumptions made about the data, such as the shape of the distribution. For instance, in linear regression, the relationship between input variables and the output is characterized by a limited number of parameters: the coefficients for each variable and an intercept.

While decision trees, cluster models, and the use of multiple variables for prediction can be elements in various machine learning approaches, they do not encapsulate the essence of parametric algorithms. Decision trees might adapt their structure based on the data and do not adhere to a fixed set of parameters, while clustering techniques can involve a varying number of clusters based on the dataset. Similarly, many algorithms, whether parametric or non-parametric, can utilize multiple variables for predictions without being defined solely by the number of parameters they produce.