In K-means clustering, what happens if K is set too high?
Correct Answer: Model overfits the data.
- Step 1: Understand what K-means clustering is. It groups data into clusters based on similarities.
- Step 2: Know that K is the number of clusters you want to create.
- Step 3: If you set K too high, you create more clusters than necessary.
- Step 4: With too many clusters, each cluster may only contain a few data points.
- Step 5: This can lead to overfitting, where the model learns noise in the data instead of the actual patterns.
- Step 6: Overfitting means the model won't perform well on new, unseen data.
- Step 7: In summary, setting K too high can make the model too complex and less useful.
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