Artificial Intelligence and Data Science · CHAPTER 1 / 5
Level: introductory undergraduate · Study session: 15–20 minutes including practice.
Learning outcome: Define a prediction task and split data to match deployment.
Before you begin: Basic algebra, SI units and a willingness to sketch the problem.
1. Understand the idea
Specify what is predicted, when the prediction occurs and which inputs are available at that moment. Remove duplicate leakage across partitions. Use chronological splitting for forecasting and group separation when repeated entities must remain independent. Fit preprocessing on training data only.
2. Write the model
Feature availability time ≤ prediction time
Define the symbols from the worked case before substituting values. Keep units consistent, record the assumptions and round only after the calculation.
3. Follow a worked example
For twelve monthly observations, an illustrative chronological split uses months 1–8 for training, 9–10 for validation and 11–12 for final testing. This tiny example teaches the split; it is insufficient evidence of a robust deployed model.
4. Explore the model
| Partition | Months | Purpose |
|---|---|---|
| Train | 1–8 | Fit parameters |
| Validation | 9–10 | Choose settings |
| Test | 11–12 | Final untouched assessment |
Compare a random split with a chronological split on trending synthetic data. Ask whether future information could reach training.
5. Reproduce it yourself
- Copy the input cases into Excel or a notebook and name each quantity and unit.
- Recalculate each result from the stated equation or rule. For qualitative cases, explain the state or partition decision instead.
- Change one input, predict the direction of change, then calculate again.
- Write one sentence explaining where the model stops being valid.
6. Check your understanding
Should a scaler use all rows before splitting?
Reveal the answer and check your work
No. Fit it on training rows and apply it to held-out rows.
Compare your result, units and reasoning. If they differ, return to the equation and test the worked case first.
7. Common mistake
An identifier or future billing field can leak the target even without an obvious duplicate.
Further study and academic note
Explore the discipline reference and further learning resources. This is an original IIMEEE introductory teaching note with synthetic examples; it is not a peer-reviewed research result or a professional design specification. Figures were constructed from the listed teaching cases. Resource links support further study and do not imply endorsement.
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