Question7 Which of the following statements is/are INCORRECT? (you can choose more than one) Choosing different distance measures affect the decision boundary in both 1-NN and 5-NN 5-NN is more robust to outliers than 1-NN 5-NN has lower bias than 1-NN 5-NN handles noise better than 1-NN ResetMaximum marks: 2 Flag question undefined

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BlackTom AI · Accepted Answer

Question context: Identify which statements are INCORRECT about k-NN variations.
Option 1: 'Choosing different distance measures affect the decision boundary in both 1-NN and 5-NN' This is correct in practice: the choice of distance metric (e.g., Euclidean, Manhattan) changes which neighbors are considered nearby, thereby altering the decision boundary for any k, including 1-NN and 5-NN.
Option 2: '5-NN is more robust to outliers than 1-NN' Increasing the number of neighbors tends to dilute the influence of any single outlier, making the classifier more robust to outliers in many datasets. This statement aligns with common intuition about bias-variance trade-offs in k-NN, where larger k reduces variance and can improve resilience to anomal points.
Option 3: '5-NN has lower bias than 1-NN' Bias in k-NN generally increases with k because the model relies on the average label of a larger local region, smoothing out details. Therefore, 5-NN typically has higher bias than 1-NN, not lower. This makes the statement inaccurate regarding the bias behavior of increasing k.
Option 4: '5-NN handles noise better than 1-NN' With more neighbors, the influence of a few mislabeled or noisy instances is diminished, so the method can be more robust to label noise and random fluctuations, especially when the data is not perfectly separable. This aligns with typical expectations that larger k can smooth over noise, although there are scenarios where it may also blur true boundaries.

In summary, evaluating each option shows that the third statement contradicts the standard bias-variance relationship in k-NN, while the others express conventional behaviors of distance choice, outlier robustness, and noise handling. The reasoning above highlights why the third option is inconsistent with how 1-NN and 5-NN typically behave.

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