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Fix schema

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uri 2025-04-15 13:37:57 +02:00
parent a2cad661dd
commit e981cc1739
1 changed files with 4 additions and 4 deletions
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models/intermediate/cross/schema.yml
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@ -3123,8 +3123,8 @@ models:
description: | description: |
Precision score, or positive predictive value. This corresponds to the Precision score, or positive predictive value. This corresponds to the
proportion of all predicted positives that were classified correctly as a proportion of all predicted positives that were classified correctly as a
positive. In our case, it answers the question "what fraction of positive. In our case, it answers the question "what fraction of Bookings
claims/payouts flagged as at risk were actually at risk?". flagged as at Risk actually generated a Claim/Payout?".
This is the count of true positives divided by the sum of true positives and This is the count of true positives divided by the sum of true positives and
false positives. Precision improves when false positives decrease. false positives. Precision improves when false positives decrease.
A hypothetical perfect model would have zero false positives, and thus a A hypothetical perfect model would have zero false positives, and thus a
@ -3135,8 +3135,8 @@ models:
description: | description: |
False positive rate, or fall-out. This corresponds to the proportion of all False positive rate, or fall-out. This corresponds to the proportion of all
actual negatives that were classified incorrectly as a positive. It can be seen actual negatives that were classified incorrectly as a positive. It can be seen
as a probability of false alarm: in our case, it answers the question "what as a probability of false alarm: in our case, it answers the question "what
fraction of non-claims/payouts were flagged as at risk?". fraction of bookings without a claim/payout were flagged as at risk?".
This is the count of false positives divided by the sum of true positives and This is the count of false positives divided by the sum of true positives and
false positives. false positives.
A hypothetical perfect model would have zero false positives, and thus a A hypothetical perfect model would have zero false positives, and thus a