In the context of regression, which metric measures the average squared differen

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Question: In the context of regression, which metric measures the average squared difference between predicted and actual values?

Options:

Correct Answer: Mean Squared Error

Solution:

Mean Squared Error (MSE) measures the average of the squares of the errors, indicating the quality of a regression model.

In the context of regression, which metric measures the average squared difference between predicted and actual values?

In the context of regression, which metric measures the average squared difference between predicted and actual values?

Step 1: Understand that regression is a method used to predict values based on input data.
Step 2: Know that when we make predictions, they may not always be correct.
Step 3: The difference between the predicted value and the actual value is called an 'error'.
Step 4: To measure how good our predictions are, we can look at these errors.
Step 5: Instead of just looking at the errors, we square them to make sure they are all positive.
Step 6: After squaring the errors, we find the average of these squared values.
Step 7: This average of the squared errors is called Mean Squared Error (MSE).
Step 8: A lower MSE value indicates a better quality of the regression model.

Mean Squared Error (MSE) – MSE is a common metric used in regression analysis to quantify the difference between predicted values and actual values by averaging the squares of these differences.