Which evaluation metric is best suited for regression tasks?

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Question: Which evaluation metric is best suited for regression tasks?

Options:

Correct Answer: Mean Absolute Error

Solution:

Mean Absolute Error (MAE) is commonly used for regression tasks as it measures the average absolute difference between predicted and actual values.

Which evaluation metric is best suited for regression tasks?

Which evaluation metric is best suited for regression tasks?

Step 1: Understand what regression tasks are. Regression tasks involve predicting a continuous value, like predicting the price of a house.
Step 2: Learn about evaluation metrics. These are tools used to measure how well a model is performing.
Step 3: Identify common evaluation metrics for regression. Some common metrics include Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
Step 4: Focus on Mean Absolute Error (MAE). MAE calculates the average of the absolute differences between predicted values and actual values.
Step 5: Understand why MAE is useful. It gives a clear idea of how far off predictions are from actual values, without squaring the errors, which can exaggerate larger errors.

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