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Start with a Prediction

Check predictions against reserved observations

BOTIE

How far were the predicted positions from the reserved observations? Build their mean squared error. Reuse the averaging calculation and add a way to measure mistakes.
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0 passed · 0 failed · 1 not run

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Examples, experiments and workspace controls
Live values

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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.

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Context for this challenge

How far were the predicted positions from the reserved observations? Build their mean squared error. Reuse the averaging calculation and add a way to measure mistakes.

The fitted line has made predictions for reserved positions. Botie needs to quantify those disagreements without letting the reserved answers influence the fit.

Subtract each reserved position from its aligned prediction, square the errors and average all of them.

Examples and results
Predicted positions
  1. 02
  2. 15
  3. 27
Reserved observed positions
  1. 03
  2. 13
  3. 28
Required result
2
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.

Opposite misses around a correct prediction

This check uses the pieces from “Measure disagreement on reserved observations”.

BOTIE

Two reserved predictions miss by equal amounts in opposite directions, while the third is correct. Predict the mean squared position error.
Predicted positions
  1. 08
  2. 12
  3. 25
Reserved observed positions
  1. 05
  2. 15
  3. 25

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.

If the same arithmetic were applied to training observations, which claim about the score would stop being justified?

A metric's meaning depends on where its examples came from as well as on its formula.

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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