Data Preparation
Measure the missing feature
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
Try a negative instrument reading, then its positive counterpart. What information disappears when we square them? Would a model need the original reading as well as its square?
Context for this challenge
How can we give the model a feature that grows faster at larger readings? Construct the square of the instrument reading. You may connect the same source to more than one input.
The instrument's response can change faster at larger readings. Botie will construct an additional input that allows a later model to represent that pattern.
Connect the same reading to both inputs of a multiplication.
Examples and results
- Instrument reading
2
- Required result
4
- Instrument reading
3
- Required result
9
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 instrument goes below its reference
This check uses the pieces from “Give the instrument reading a squared companion”.
BOTIE
- Instrument reading
-2.5
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.
How could a later fitted coefficient use this feature, and what has this workshop deliberately not estimated?
Feature construction supplies possible relationships; fitting determines how those features contribute to predictions.
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 →