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KNN method is non-parametric and becomes more flexible as K increases.
True: The KNN method is indeed an example of non-parametric approaches. This means it does not make strict assumptions about the form of the underlying data distribution.
True: As K increases in the KNN method, it does become more flexible. Higher values of K lead to smoother decision boundaries, reducing the impact of noise.
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