The words on this site, defined.

The breakdown of which inputs pushed a single prediction up or down, and by how much. Distinct from feature importance, which describes the model overall: attribution is per-row, and it is what lets somebody argue with a specific answer rather than accept or reject the whole model.

Used here: Explainability in practice →

How often the thing happens at all, before any model looks at it. A model that predicts a 2%-prevalence event can be 98% accurate by predicting "no" every time, which is why accuracy on rare events is close to meaningless and why we quote precision and recall instead.

Whether a stated probability means what it says: of everything the model called 70%, does roughly 70% actually happen? A model can rank cases perfectly and still be badly calibrated, and calibration is what matters the moment somebody uses the number to decide how much to spend.

When the outcome you care about is rare — failures, fraud, readmissions. It makes ordinary accuracy misleading, distorts training unless handled, and is the normal condition in almost every problem worth modelling.

Used here: The data problem, honestly →

When the relationship a model learned stops holding because the world moved: new equipment, a changed process, a different population. The model does not announce it. Accuracy decays quietly, which is why monitoring is part of delivery rather than a later phase.

Linking records by rules that must hold exactly — same identifier, same date of birth and postcode. Fast, auditable, and it misses everything with a typo. Usually the first pass before probabilistic and spatial methods handle the remainder.

Used here: Six sources, no shared key →

Deciding which records across different systems refer to the same real thing when nothing shares an identifier. The most consistently underestimated part of applied analytics: every downstream model inherits its errors, and neither a missed match nor a wrong one shows up in the output as an error.

Used here: Entity resolution at national scale →

Models whose outputs can be accounted for in terms a domain expert can check. In our work it is a deliverable written into milestones, not a courtesy — a prediction nobody senior can defend does not get acted on, however accurate it is.

A single measured input a model reads. Most of the effort in a real project goes into constructing features from raw records rather than into choosing an algorithm, which is the reverse of how projects are usually described.

The recorded answer a model is trained and scored against. It is a record of what somebody wrote down, not of what happened — and where the two differ, the model learns the recording process. Worth auditing before the model is.

The North American Industry Classification System code identifying what a business does. Federal solicitations name one, and it decides both who is eligible and what size standard applies to them.

Used here: Our NAICS codes →

When a model learns the noise in its training data and so performs far better there than on anything new. The reason a number quoted without saying which data it was measured on tells you nothing.

Precision: of the cases flagged, how many were real. Recall: of the real cases, how many were flagged. They trade against each other, and which one matters is a business decision — a false alarm and a missed failure rarely cost the same.

The prime holds the contract with the government and carries the delivery risk; a subcontractor delivers through the prime. Worth stating which you were, because past performance means different things in each case.

Used here: Past performance →

Matching records by scoring how much their fields agree and setting a threshold, rather than demanding exact equality. It handles typos and naming variants, and it produces a match probability you can tune — which means somebody has to choose where a maybe becomes a yes.

How much service a piece of equipment has left before failure. Useful output is a window with an uncertainty attached rather than a date, because a maintenance planner needs to know how wrong the estimate could be to schedule around it.

Used here: Preventive maintenance →

A contract or portion of one reserved for a category of small business. It determines which vendors may compete at all, so it is usually the first thing a contracting officer establishes and the first thing a vendor checks.

Used here: Capabilities statement →

A method that assigns each input a contribution to one prediction, derived from cooperative game theory so the contributions add up to the prediction itself. What sits behind most per-row attribution you will see, including ours.

A table that is almost entirely empty — every buyer against every property, where almost no pair has an interaction. Recommenders live here, and the emptiness is the problem to solve rather than a data-quality defect to fix.

Used here: A 99.35% empty matrix →

The two identifiers a federal buyer uses to confirm a vendor is real and registered: the Unique Entity ID from SAM.gov, and the Commercial and Government Entity code. Both of ours are published rather than offered on request.

Used here: Capabilities statement →