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Answer: All of the above, as Spark MLlib supports various evaluation metrics for regression tasks.
In a Spark MLlib project, various evaluation metrics can be used for regression tasks to assess the performance of the machine learning model. Mean Absolute Error (MAE) measures the average absolute difference between the predicted and actual values, providing a measure of the average error magnitude. Mean Squared Error (MSE) measures the average squared difference between the predicted and actual values, penalizing larger errors more heavily. R-squared measures the proportion of the variance in the target variable that is explained by the model, providing a measure of the model's goodness of fit. Spark MLlib supports these evaluation metrics, allowing users to choose the appropriate metric based on their specific requirements and dataset characteristics.
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In a Spark MLlib project, you are working with a large dataset and need to perform model evaluation to assess the performance of your machine learning model. Which of the following evaluation metrics can be used in Spark MLlib for regression tasks, and how do they work?
A
Mean Absolute Error (MAE), which measures the average absolute difference between the predicted and actual values.
B
Mean Squared Error (MSE), which measures the average squared difference between the predicted and actual values.
C
R-squared, which measures the proportion of the variance in the target variable that is explained by the model.
D
All of the above, as Spark MLlib supports various evaluation metrics for regression tasks.
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