Choosing and Checking a Model
Audit one group of failure probabilities
When the group averages agree
Botie's failure forecast says 80 percent for every sensor in a group. The observed outcomes give us a way to audit what that confidence meant.
BOTIE
The inputs we are comparing
Failure probabilities in one group
Confident group
- 00.8
- 10.8
- 20.8
- 30.8
Lower-risk group
- 00.25
- 10.25
- 20.25
- 30.25
Observed failures
Confident group
- 01
- 10
- 21
- 30
Lower-risk group
- 00
- 11
- 20
- 30
Result for confident group
[
[
0.8,
0.5
],
0.34
]The group agrees, but individual sensors still fail
This check uses the pieces from “Keep confidence, frequency and error separate”.
BOTIE
- Failure probabilities in one group
- 00.6
- 10.6
- 20.6
- 30.6
- 40.6
- Observed failures
- 01
- 11
- 20
- 31
- 40
Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.
Build a construction in the workspace above first. This case will test those pieces.
What did the group-average comparison reveal that the Brier score alone did not?
Keep probability reliability separate from the threshold used to act on a forecast.
These tiny groups illustrate the arithmetic. They do not estimate calibration precisely or fit a correction.
Work through the Python challenges →