CODEWITHSUNDEEP
machine-learningscikit-learndecision-tree
Ahmad Anis
Ahmad Anis

Reputation: 2714

non-parametric supervised learning method

In the documentation of Scikit-Learn Decision Trees, it is stated that:

Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

What is meant by non-parametric supervised learning?

Upvotes: 0

Views: 326

Answers (1)

OmG
OmG

Reputation: 18838

non-parametric is on the opposite side of parametric. In a parametric learning model, you can describe the set of the hypothesis (or learning model) as a function of a finite number of parameters such as SVM. Hence, a non-parametric model can be seen as a model with an infinite number of parameters to be described, i.e., the distribution of data cannot be defined by a finite set of parameters [1].

[2] An easy to understand nonparametric model is the k-nearest neighbors algorithm that makes predictions based on the k most similar training patterns for a new data instance. The method does not assume anything about the form of the mapping function other than patterns that are close are likely to have a similar output variable.

Upvotes: 1

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