decision boundary concept
| Definition | The set of inputs whose score equals the threshold. Chapter 6. |
|---|---|
| Example | For the score 2x1 + x2 − 3 at threshold zero, the boundary is the line 2x1 + x2 = 3. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id decision-boundary, kind concept. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | none |
| Assumptions and scope |
|
| Prior art | none recorded |
| Evidence | lean/DataMiningAsObservation/DecisionBoundary.lean |
| Reviewed | not yet reviewed; generated 2026-09-10 from records at the commits on the provenance page. |
Equation
none
Conditions
- The set of inputs whose score equals the threshold. For a linear classifier it is an affine hyperplane. Moving orthogonally to the weights never crosses it, moving along the weights crosses it exactly once, and every boundary point has the same projection on the weights.
- The boundary is where the read direction and the threshold meet and nothing else about the input enters it, which is the classifier’s nuisance drawn as a picture.
Conditions are curated in entries.toml rather than read
from a record.
Ledger
none
First stated
Chapter 6 section 6.1 of Data Mining as Observation, where the classifier’s read direction and its threshold meet.
Measurements
none
Failures and corrections
none
Invariance envelope
none declared
Machine checked
lean/DataMiningAsObservation/DecisionBoundary.lean,
theorems orth_stays, cross_once,
same_projection, at observation-data-mining f3914f0; what
the check covers is stated in the book’s appendix
C.
Used in
Data Mining as Observation chapters 0.
Related
classifier; threshold; margin; nuisance.
See also
Book equations stated beside the entry’s terms, not defining it: 6.1, 6.2.
Ledger rows that cite the entry’s records without naming it: GO-1.
Status
Generated 2026-09-10 by encyclopedia/generate.py; book
at observation-data-mining f3914f0; the commit of every record is listed
in the encyclopedia’s provenance.