Classical Machine Learning
Classify a specimen with two questions
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
Which specimens belong to class 1? Our supplied tree requires a stem longer than 3 cm and a leaf narrower than 4 cm. Return 1 when both conditions hold and 0 otherwise.
The supplied specimen rule asks two strict measurement questions. Botie must preserve each boundary and require both answers to be yes.
Test stem length greater than 3 and leaf width less than 4, then combine the two binary answers so both must hold.
Examples and results
- Stem length (cm)
2
- Leaf width (cm)
2
- Required result
0
- Stem length (cm)
5
- Leaf width (cm)
3
- Required result
1
- Stem length (cm)
6
- Leaf width (cm)
5
- Required result
0
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.
The leaf width reaches the branch boundary
This check uses the pieces from “Keep the stem and leaf tests separate”.
BOTIE
- Stem length (cm)
5
- Leaf width (cm)
4
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.
Why does multiplying the two binary answers express 'both', and which extra task would be required to learn these questions from labelled specimens?
Separate executing a decision rule from selecting its thresholds and structure during training.
This is a small construction problem. The lesson explains the assumptions and the parts of the full method that this example leaves out.
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