Frame the regression loss workbench experiment

Success is not “I followed the tutorial.” Success is producing evidence that: both losses are computed from identical residuals and the outlier effect is visible. The accepted input is narrow on purpose: predicted vs true numeric targets for a tiny regression set. That narrowness is what lets you inspect every field and prevents a toy demo from being narrated as a production system.

Record the baseline you must beat: mean prediction residual before any model. If the finished artifact cannot beat that baseline on the fixture below, stop and revise the claim before writing more code.

truth=[2,4,6,8]; pred=[2.5,3.5,6.5,12]
errors=[p-y for y,p in zip(truth,pred)]
print(errors)

Expected evidence: four signed residuals, with 4.0 largest. Treat the printout as a claim about this fixture, not as proof that the toolchain merely started.

For the regression loss workbench, a decorative win often looks like a clean run that never checks MSE and MAE printed; removing the outlier changes MSE more than MAE. Write the metric down now so later pages cannot redefine success after the fact. Also note the operational threat you will eventually gate on: optimizing loss on a biased slice and calling it global quality.

For loss-functions, write the claim on a sticky note in this exact shape: “Given predicted vs true numeric targets for a tiny regression set, the regression loss workbench will …”. Fill the ellipsis with the observable part of: both losses are computed from identical residuals and the outlier effect is visible. Tape the baseline beside it: mean prediction residual before any model. If someone later replaces your metric with a vibe check, the sticky note is how you push back.

Also sketch the one-sentence user story: a person uses this output to compare MSE vs MAE on the same residual set to see which outlier hurts more. If that sentence needs a dashboard, a model zoo, or five services, the lab scope is too wide—shrink the fixture (y_true/y_pred arrays including one large outlier) until the story fits on one screen.

Decide now whether live network calls are allowed on page 1. For this lab they usually are not; inventory and contracts should run offline against y_true/y_pred arrays including one large outlier. Note the metric you will eventually require (MSE and MAE printed; removing the outlier changes MSE more than MAE) so page 4 cannot invent a softer target. The characteristic failure to keep in mind is shape mismatch between y_true and y_pred, or silent NaN loss.

At the experiment brief stage for loss-functions, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about y_true/y_pred arrays including one large outlier that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: mean prediction residual before any model.

For this page specifically, success looks like a falsifiable claim and baseline written before coding while still centering the user decision to compare MSE vs MAE on the same residual set to see which outlier hurts more. If you cannot point to a file, command, or assertion that proves that for the regression loss workbench, stay on this page instead of advancing.

Glossary: loss function · Glossary: gradient descent

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