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In the context of Spark MLlib, compare and contrast the scalability of linear regression and decision trees. Provide examples of scenarios where one algorithm may be more suitable than the other based on the size and complexity of the dataset.
A
Linear regression is more scalable than decision trees, as it requires less computational resources and can handle larger datasets.
B
Decision trees are more scalable than linear regression, as they can handle non-linear relationships and complex interactions between features.
C
Both linear regression and decision trees are equally scalable, as Spark MLlib provides distributed implementations of both algorithms.
D
The scalability of linear regression and decision trees depends on the specific characteristics of the dataset, such as the number of features, the presence of non-linear relationships, and the size of the dataset.
