In regression tasks, which metric is typically used to measure the difference be

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Question: In regression tasks, which metric is typically used to measure the difference between predicted and actual values?

Options:

Correct Answer: Mean Absolute Error

Solution:

Mean Absolute Error (MAE) is used in regression tasks to quantify the average absolute difference between predicted and actual values.

In regression tasks, which metric is typically used to measure the difference between predicted and actual values?

In regression tasks, which metric is typically used to measure the difference between predicted and actual values?

Step 1: Understand that regression tasks involve predicting a continuous value.
Step 2: Know that we need a way to measure how close our predictions are to the actual values.
Step 3: Learn about Mean Absolute Error (MAE), which is a common metric used for this purpose.
Step 4: Realize that MAE calculates the average of the absolute differences between predicted values and actual values.
Step 5: Remember that a lower MAE indicates better predictive accuracy.

Mean Absolute Error (MAE) – MAE is a metric used in regression analysis to measure the average magnitude of errors in a set of predictions, without considering their direction.