Epäparametriseksi

Epäparametriseksi is a Finnish term that translates to "non-parametric" in English, commonly used in statistical and machine learning contexts. It refers to methods or models that do not make assumptions about the underlying distribution of the data. Unlike parametric methods, which assume the data follows a specific distribution (e.g., normal distribution), non-parametric methods are more flexible and can adapt to a wider variety of data shapes.

In statistics, non-parametric tests are used to analyze data when the assumptions of parametric tests, such as normality, are not met. Examples include the Wilcoxon rank-sum test (a non-parametric alternative to the t-test) and the Kruskal-Wallis test (a non-parametric alternative to ANOVA). These tests often rely on ranks or other order statistics rather than directly on the observed values.

In machine learning, non-parametric models are characterized by their ability to grow in complexity as more data becomes available. Their structure is not fixed beforehand but is determined by the data itself. This contrasts with parametric models, which have a fixed number of parameters regardless of the dataset size. Examples of non-parametric machine learning algorithms include k-nearest neighbors (k-NN), decision trees, and support vector machines (SVMs) with certain kernel choices. The flexibility of non-parametric approaches makes them powerful for exploring complex relationships in data without prior distributional assumptions.