Define the ML Problem
Cost the inspection policy
Compare the candidate with the heat rule
Botie can compare a heat rule with a candidate model only after charging both for inspections and missed faults on the same pump records.
BOTIE
The inputs we are comparing
Confirmed faults, 1 = faulty
Same in both cases
- 01
- 10
- 21
- 30
Inspection decisions
Heat rule
- 01
- 11
- 20
- 30
Candidate model
- 01
- 10
- 21
- 31
Cost per inspection
Same in both cases
3
Cost per missed fault
Same in both cases
20
Result for heat rule
26
Inspecting everything avoids misses but still costs money
This check uses the pieces from “Price each consequence of the inspection policy”.
BOTIE
- Confirmed faults, 1 = faulty
- 00
- 11
- 20
- 31
- Inspection decisions
- 01
- 11
- 21
- 31
- Cost per inspection
4
- Cost per missed fault
30
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.
Which omitted cost or operating constraint could reverse the preference between the rule and model?
Choose a learned model for measured decision benefit under stated assumptions, and preserve the simpler alternative for comparison.
Fault labels are supplied for all pumps. This omits uncertain inspection effectiveness, upkeep, and limited capacity.
Work through the Python challenges →