Applied Case — The Honest Tabular Model, End to End

P5.applied-cases.01 · Audience: guest, it-ml, language-pro · Prerequisites: Model Evaluation, Selection & Interpretability, Evaluation Metrics for NLP

LLM & Generation Evaluation Applied Case — Clustering & Segmentation

LLM grading (this page):Real gradingOff — no LLM calls

Everything the ML Methods and Evaluation tracks teach, run once, in order, on real data. The guided walk below builds a species classifier on the Palmer penguins study table with every number shown; the practice ladder makes you rebuild it rung by rung in the kernel — real files, real missing values, resource-metered grading — and ends in the platform's first Project: the same arc on 32,561 census rows with genuine missingness and class imbalance, graded by hidden tests and reviewed against a design rubric.

Step 1 / 8 — Frame the task — and meet the data

Task: predict a penguin’s species(Adelie / Gentoo / Chinstrap) from its measurements — a supervised, multi-class classification problem, exactly the shape catalogued in Supervised Learning Algorithms. The study table has 344 labelled rows and 8 columns; some measurements are missing, and sex is missing on 11 rows.

Column	Type	Missing
`species` (target)	categorical, 3 classes	0
`island`	categorical, 3 values	0
`bill_length_mm`, `bill_depth_mm`, `flipper_length_mm`, `body_mass_g`	numeric	2 each
`sex`	categorical, 2 values	11

Class mix: Adelie 44.2 %, Gentoo 36.0 %, Chinstrap 19.8 %. Not balanced — every later choice (stratified split, baseline, metric reading) has to respect that.

Why does framing come before any code — what could go wrong if you decided the task after exploring the table?

📚 Go Further

Type	Resource
In-app	P5.ml-methods.06 — Model Evaluation, Selection & Interpretability — the splitting, CV, leakage and selection theory this case exercises
In-app	P5.evaluation.01 — Evaluation Metrics — precision/recall/F1, ROC/AUC and calibration, with the interactive threshold explorer
In-app	P5.applied-cases.04 — The Leakage Clinic — what happens when the discipline above is skipped, three ways
External	scikit-learn user guide — Pipelines and composite estimators; Common pitfalls: data leakage
External	Mitchell et al. (2019), Model Cards for Model Reporting — the template step 8 abbreviates

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🎓 Practice ladder

5 graded rungs · ~97 min

Now build it yourself, in the kernel — real files under /data, real missing values, resource-metered grading. Four guided rungs rebuild the penguins walk; the finale is the platform's first Project: the full arc on the UCI Adult census table, graded by hidden tests and reviewed against the four-criterion design rubric shown in the workspace.

Rung 1 — Split before you look

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Rung 2 — Baselines before models

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Rung 3 — Preprocessing belongs in the pipeline

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Rung 4 — Compare models with cross-validation

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Rung 5 — Project: the honest income model

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✅ Key takeaways

The order is the method: frame → split → explore (train only) → baseline → pipeline → cross-validate → threshold → error analysis. Every stage exists to protect the honesty of the final number.
Split before you look, stratified on the target — 275/69 on penguins — and spend the test set exactly once.
Preprocessing lives inside the Pipeline: imputers and encoders fit on training rows only, and prediction time replays the identical transformation (leakage-proof and serving-proof at once).
Model selection runs on cross-validated training folds (logistic 0.9927 vs tree 0.9527); the test split votes zero times in any decision.
On imbalanced data the threshold is a product decision: sweeping the precision-recall curve on the census model lifts positive-class F1 from 0.6734 to 0.6941 over the 0.5 default.
The deliverable is model + card: data, split protocol, metrics, intended use, explicit non-uses — the artefact P13's serving-and-drift case picks up.

Next: P5.applied-cases.04 — The Leakage Clinic: three notebooks that score brilliantly and would fail in production; find each leak, fix it, and reproduce the honest number.

Try it yourself

A scratch console for this page's ideas — ungraded, nothing you run here is recorded.

Scratch console

A scratch console with the scientific stack (pandas, numpy, scikit-learn). Runs on the server — no network, resource-limited and measured.

Output appears here.

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