Occam's Razor is a philosophical principle that suggests that among competing hypotheses, the one with the fewest assumptions should be selected. This principle is particularly relevant in statistical modeling, where it emphasizes simplicity and parsimony, guiding model selection by favoring models that explain the data adequately without unnecessary complexity. By aligning with this principle, practitioners can avoid overfitting and enhance the interpretability of their models.
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