In a Spark MLlib implementation, you are working with a large dataset and need to build a model for a binary classification task. Which of the following algorithms would be most suitable for this task, and why?

Simulated

Linear regression, as it can be used for both regression and classification tasks.

3.8%

Decision trees, as they can handle non-linear relationships and complex interactions between features.

13.8%

Logistic regression, as it is specifically designed for binary classification tasks and provides a probabilistic interpretation of the predictions.

60.8%

Random forests, as they are an ensemble method that combines multiple decision trees to improve accuracy and robustness.

21.5%

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