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What are the assumptions in a parametric procedure?
Parametric statistical procedures rely on assumptions about the shape of the distribution (i.e., assume a normal distribution) in the underlying population and about the form or parameters (i.e., means and standard deviations) of the assumed distribution.
How are parametric methods classified in introductory statistics?
Methods are classified on the basis of what we know about the population we are studying. Parametric methods are typically the first methods studied in an introductory statistics course. The basic idea is that there is a set of fixed parameters that determine a probability model.
How are robust parametric statistics used in the real world?
Robust parametric statistics can proceed in two ways: by designing estimators so that a pre-selected behaviour of the influence function is achieved
What are the parameters of a nonparametric test?
parameters (i.e., means and standard deviations) of the assumed distribution. Nonparametric statistical procedures rely on no or few assumptions about the shape or parameters of the population distribution from which the sample was drawn. Parametric tests and analogous nonparametric procedures
How are parametric and nonparametric models used in machine learning?
Decision Trees like CART and C4.5 Parametric models deal with discrete values, and nonparametric models use continuous values. Parametric models are able to infer the traditional measurements associated with normal distributions including mean, median, and mode.
Why is feature engineering important in parametric models?
While some nonparametric distributions are normally oriented, often one cannot assume the data comes from a normal distribution. Feature engineering is important in parametric models. Because you can poison parametric models if you feed a lot of unrelated features. Nonparametric models handle feature engineering mostly.