Classical Machine Learning
Compare people on a common scale
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
How far apart are these two people on the standardised measurements? Build Manhattan distance by adding the absolute height and weight differences. A negative difference must not cancel a positive one.
Height and weight differences are already standardised. Botie wants both coordinate separations to count even when their signs oppose each other.
Take the absolute value of each supplied standardised difference and sum the resulting magnitudes.
Examples and results
- Standardised height and weight differences
- 00.3
- 10.4
- Required result
0.7
- Standardised height and weight differences
- 00.1
- 1-0.5
- Required result
0.6
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.
Both standardised differences are negative
This check uses the pieces from “Add separation without sign cancellation”.
BOTIE
- Standardised height and weight differences
- 0-0.6
- 1-0.8
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 a negative coordinate difference still increase distance, and what relationships between measurements does this metric ignore?
A distance rule combines coordinate differences according to a chosen geometry and scaling convention.
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 →