Start with a Prediction
Count the right mistakes
BOTIE
Examples, experiments and workspace controls
Connect a tool's inputs to see what it produces. You can try a branch before connecting it to Result.
Try your own inputs
Use a copy of the example to try your own data. Keep the problem's assumptions in mind, and predict the effect of a change before checking the output. You can also add another branch to compare two ways of doing the calculation.
Context for this challenge
Of the reported lights, what fraction were real? Build precision from the counts of true detections and false alarms. The rule must work for each set of observations.
The faint-light experiment produced true detections and false alarms. Botie wants to know how trustworthy a reported light was.
Add true detections and false reports, then divide true detections by that total.
Examples and results
- True light detections
3
- False light reports
2
- Required result
0.6
- True light detections
4
- False light reports
1
- Required result
0.8
Try a fresh construction
This clears this draft, including its undo history. Earlier successful attempts stay recorded. Build again without opening hints to record an independent attempt.
Stuck? Inspect a worked construction
The tools below form one possible solution. You can assemble it, follow its signals, and undo that change to return to your own construction.
Assumptions and test precision
This is a small construction problem. The lesson explains the assumptions and the parts of the full method that this example leaves out. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.
Every positive report was false
This check uses the pieces from “Use all positive reports as the reference group”.
BOTIE
- True light detections
0
- False light reports
7
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 missing count would you need to measure recall, and why would adding it to this denominator change the question?
Choose an evaluation metric by the mistake question it answers, then identify the required counts.
This is a small construction problem. The lesson explains the assumptions and the parts of the full method that this example leaves out.
Work through the Python challenges →