Intermediate
Artificial Intelligence & Machine Learning
Q64 / 100
What is mean squared error (MSE) vs mean absolute error (MAE)?
Correct! Well done.
Incorrect.
The correct answer is B) MSE penalizes large errors more heavily (squaring amplifies outliers); MAE treats all errors equally, being more robust to outliers
B
Correct Answer
MSE penalizes large errors more heavily (squaring amplifies outliers); MAE treats all errors equally, being more robust to outliers
Explanation
MSE = mean((y-ŷ)²): sensitive to outliers (squared amplification). MAE = mean(|y-ŷ|): more robust. Huber loss combines both: quadratic for small errors, linear for large. Choice depends on tolerance for outliers.
Progress
64/100