Which of the following algorithms is particularly challenging to scale for large datasets?
- Decision Trees
- K-Nearest Neighbors
- Naive Bayes
- Support Vector Machines
Decision Trees can be challenging to scale for large datasets because they tend to create complex trees that require a lot of memory and computational resources. They can easily overfit, making them less suitable for big data scenarios.
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