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Classical Machine Learning

Recalculate the group centres

BOTIE

Select point rows with group label 0 and average their feature columns.
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0 passed · 0 failed · 1 not run

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Context for this challenge

Select point rows with group label 0 and average their feature columns.

We want representative centres for groups of similar observations. The current assignments are supplied, so our job is to find a new centre for each group. This is the update that follows an assignment step, and the new centres would be used to assign the observations again.

Inspect the component calculation
Mean of each column(
  Example rows: Keep rows with a label(
    Rows: Point rows
    Group labels: Current group labels
    Group: 0
  )
)
Examples and results
Point rows
10
32
70
92
Current group labels
  1. 00
  2. 10
  3. 21
  4. 31
Required result
  1. 02
  2. 11
Try a fresh construction

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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.

Your last tested pieces can start the full construction. An existing draft there keeps its own pieces.

One group contains three points and the other one

This check uses the pieces from “Keep both groups' coordinate means”.

BOTIE

The current assignments are uneven. Predict both updated centres in group order, using each group's own count.
Point rows
00
30
06
128
Current group labels
  1. 00
  2. 10
  3. 20
  4. 31

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 step should use these new centres next, and why would a single mean across all points destroy the supplied group distinction?

An iterative clustering procedure alternates assigning observations and recomputing representatives; this workshop performs the latter.

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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