Classical Machine Learning
Let the estimators share the load
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
Three fitted models estimate the position of the same object. Build an equal-weight average using the tools from statistics.
Three models trained on resampled observations estimate the same object's position. Botie will combine their predictions with equal weight.
Total the supplied model predictions and divide by the number of predictions.
Examples and results
- Predictions from three models
- 02
- 14
- 29
- Required result
5
- Predictions from three models
- 03
- 16
- 29
- Required result
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.
One model disagrees sharply
This check uses the pieces from “Give every fitted model an equal contribution”.
BOTIE
- Predictions from three models
- 04
- 14
- 219
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 facts about the individual predictions disappear in their mean, and what evidence would you need to claim the ensemble predicts better?
Separate creating varied fitted estimators, aggregating their predictions and evaluating the resulting ensemble.
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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