In K-means clustering, what happens if K is set too high?

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Q: In K-means clustering, what happens if K is set too high?

Solution: If K is set too high, the model may overfit the data, resulting in too many clusters that do not generalize well.

Steps: 7

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.