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Answer: Scikit-learn is limited by single-node processing; Spark ML leverages cluster computing to handle large datasets.
Scikit-learn, being a single-node solution, faces limitations when dealing with large datasets that exceed the memory and processing capabilities of a single machine. Spark ML, on the other hand, is designed to distribute data and computation across a cluster of machines, enabling it to handle much larger datasets and more complex computations by leveraging parallel processing capabilities.
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Explain the difference between using scikit-learn and Spark ML for a machine learning task involving a large dataset. What are the limitations of scikit-learn in this context and how does Spark ML overcome these limitations?
A
Scikit-learn is more efficient for large datasets; Spark ML provides scalability through distributed processing.
B
Scikit-learn is limited by single-node processing; Spark ML leverages cluster computing to handle large datasets.
C
There is no significant difference; both can handle large datasets effectively.
D
Spark ML is limited by its complexity; scikit-learn is simpler and more efficient.