Ship and explain the two-cluster segmenter

A shippable lab artifact includes: how to run it, the metric result (silhouette or inertia recorded; centers have finite values), the failure you can still reproduce (k larger than n, or scaling skipped so one feature dominates), the security gate for joining cluster IDs back to raw emails in an unsecured export, and a rollback note. The user decision it supports remains: group customers by spend/visits without pretending clusters are ground-truth labels.

report={'seed':7,'sizes':[3,3],'wcss':0.031,'interpretation':'low/high activity hypotheses'}
assert sum(report['sizes'])==6
print(report)

Expected evidence: stage evidence for mastery ship. Store this beside the fixture version so scores remain meaningful after content changes in clustering-basics.

State operating limits for the two-cluster segmenter in plain language: fixture size, offline vs live dependencies, and what would require a new eval set. Shipping clustering-basics is honest scoping, not maximal confidence language.

Fill this table in your notes for the two-cluster segmenter:

• Proved on small 2D customer matrix: …
• Unproved beyond the fixture: …
• Metric that blocks release: silhouette or inertia recorded; centers have finite values
• Failure still reproducible: k larger than n, or scaling skipped so one feature dominates
• Security gate: joining cluster IDs back to raw emails in an unsecured export
• Rollback: …

Ship the narrative only when the unproved list is honest. Reviewers trust narrow claims that support group customers by spend/visits without pretending clusters are ground-truth labels more than maximal language that collapses under the first production oddity.

Hand your ship note to a peer and ask them to recreate a proved/unproved ship note with rollback without watching you type. If they cannot, your evidence is still tribal knowledge. Tighten the run command and the metric line until a stranger can validate the two-cluster segmenter against small 2D customer matrix.

At the mastery and shipping stage for clustering-basics, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about small 2D customer matrix 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: random two-group assignment inertia for comparison.

For this page specifically, success looks like a proved/unproved ship note with rollback while still centering the user decision to group customers by spend/visits without pretending clusters are ground-truth labels. If you cannot point to a file, command, or assertion that proves that for the two-cluster segmenter, stay on this page instead of advancing.

Glossary: clustering · Cheatsheet: ML Python starter

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