Nonparametric modeling is a statistical approach that does not rely on predefined assumptions about the data distribution. Unlike parametric models, which are based on fixed parameters and specific assumptions, nonparametric models are more adaptable. These models can adjust to various data patterns without being constrained by assumptions about the population or distribution. Examples include kernel density estimation and decision trees, where the model complexity evolves with the size and nature of the data.
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