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Overfitting Diagnosis Walkthrough

Decision tree Has example

Recognize and fix a model that memorizes.

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Diagnose whether my model overfits. Model and data: [MODEL_AND_DATA]. Training and validation scores: [SCORES]. Learning curve or other evidence: [EVIDENCE].

Walk me through:
1. How to read my scores: a large gap between training and validation suggests overfitting; both poor suggests underfitting; both good but production poor suggests drift or leakage. Classify my case with the numbers.
2. The learning curve: how to plot score against training set size and what each shape means; code to produce it.
3. The validation curve for the main complexity parameter (tree depth, regularization strength).
4. A ranked list of fixes with the expected effect and the cost: more data, simpler model, regularization, early stopping, dropout (for neural networks), feature reduction, cross-validation with more folds, ensembling.
5. A table of three experiments: Change | Prediction (what I expect) | Result to record | Decision.
6. A warning about repeatedly peeking at the validation set.
Finish with a plain explanation of overfitting using an analogy a manager would understand, in under 70 words.

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Symptoms: training accuracy 99%, validation 78%.
Learning curve reading: validation flat, large gap → high variance.
Checks: 1) leakage (duplicate customers across splits); 2) too many features for 3,000 rows; 3) tree depth unlimited.
Experiments
- Limit depth to 6: validation 83%, train 90%.
- Remove 40 noisy features: validation 84%.
- More data: not available.
Conclusion: reduce complexity first; check for duplicates.
Report the final test score once, after the choices are fixed.
Next experiment: add regularisation and compare five-fold cross-validated scores; log each run in the experiment table with the seed.

• overfitting