Prediction Error
Prediction error compares a prediction with a target.
For a regression target:
error = prediction - target
If:
prediction = 8
target = 5
then:
error = 8 - 5 = 3
The sign tells direction. A positive error means the prediction is above the target. A negative error means it is below the target.
But raw error is not always a good loss. Positive and negative errors can cancel each other across a batch.
errors = [3, -3]
average error = 0
The model was wrong twice, but the average signed error says zero. That is why many losses remove the sign, often by squaring or taking an absolute value.
A model predicts 12, and the target is 9. Compute prediction - target.
Compute it first, then check your number.
HintPrediction minus target
Keep the sign.
SolutionWork it out
prediction - target = 12 - 9 = 3.
Two examples have signed errors 4 and -4. What is their average signed error?
Compute it first, then check your number.
HintAverage the two errors
Add the errors, then divide by 2.
SolutionWork it out
(4 + (-4)) / 2 = 0 / 2 = 0. The errors cancel even though both examples
were wrong.