You will be able to
- Separate training, validation and final test roles.
- Detect leakage through preprocessing and related examples.
Three different jobs
Training data fits parameters. Validation data guides choices such as model size, feature set and thresholds. A final held-out test estimates performance after those choices are fixed. Repeatedly choosing by test results turns that set into validation data, even if it still has “test” in its filename.
Respect dependence and chronology
Audio windows from one recording, images from one patient and transactions from one person are not independent just because they are separate rows. Split by the relevant group or by time when deployment predicts future data. Fit scaling and feature selection only on training data, then apply the fitted transformation to other splits.
Worked example
A 100-row dataset split 60/20/20 has 60 training rows. If adjacent windows from the same recording appear on both sides, the nominal split can still leak information. Row counts alone are not a guarantee.
A common trap
Removing the label column is not enough to prevent leakage: future information, duplicates and group identity can reveal targets indirectly.
Predict, then experiment
Does a small fitting error prove that a model works on new recordings? Before touching the controls, say or write your prediction.
Try it yourself
Fit a straight-line model
Which slope and intercept reduce training error?
Five fixed synthetic training examples; circles are targets, solid is wx+b. No test set is shown. Units are arbitrary; MSE measures fit to these five points only.
Can you make it happen?
Find a line with training MSE below 0.15 on these five points.
Predict a setting before you try it. This optional challenge checks the current parameters, not lesson completion.
A hint
Adjust the slope first, then shift the intercept. Read the error rather than judging only by the plot.
One solution & why it works
Slope 2 and intercept 1 give MSE 0.084. This is a training-fit result, not a claim about unseen examples.
Your experiment
Change slope and intercept until the toy training MSE is small. Explain why the plot alone cannot answer the deployment question: it has no held-out dataset.
Change one parameter at a time. Compare what you observed with your prediction and record an explanation in your notes.
Check your understanding
Try from memory first. Hints and feedback are part of learning, not a penalty. These checks are for self-study, not a graded qualification.
Reference answers (after your attempt)
- Only the training split — Correct: reuse those training estimates on other splits.
- 24examples — 0.2×120=24. The grouping policy still matters.
Apply it: Draw a leakage-safe pipeline
Use paper, your notes, or a local notebook/editor. Code examples are Python 3 starting points to run yourself; this page does not execute code or automatically assess the practical task.
- Choose a dataset with groups or time.
- Specify the split before feature fitting.
- Write where scaling, tuning and the final evaluation happen.
Reference solution & review criteria
For recordings: group by recording/session before window extraction or keep all related windows in one split. Fit scaler/model on train, select using validation, evaluate once on untouched test; document group overlap checks.
Your notebook
Notes and progress stay in this browser. They are not sent to the server; ordinary web-server request logs still operate. Export a backup from the course page.
Completion requires both correct checks and your practical-task acknowledgement.
Go deeper
Sources support further study; the worked examples and questions here are original. Browser experiments are small, disclosed models—not evidence of production-model quality or hardware performance.