Practice K-nearest Neighbors (knn) (5.4) - Supervised Learning - Classification Fundamentals (Weeks 5)
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K-Nearest Neighbors (KNN)

Practice - K-Nearest Neighbors (KNN)

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Question 1

When would a large 'K' value be beneficial in KNN?
* Type: text
* Correct Answer: A large 'K' value is beneficial when the data is noisy, as it makes the model more robust to individual noisy data points by averaging predictions over more neighbors, leading to a smoother decision boundary and lower variance.
* Explanation: It provides a more generalized view, reducing the impact of outliers.
* Hint: Think about how more opinions can smooth out extreme views.

💡 Hint: Think about how more opinions can smooth out extreme views.

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