Give each contribution a weight

Change the inputs and their weights. See which contributions shape the normalized result.

Teaching model · computed in your browser

Build a weighted score

Change component values and weights. Watch normalization turn them into a single, explainable score.

S=\sum_i\left(\frac{w_i}{\sum_j w_j}\right)v_i

vᵢ is a component value from 0 to 1; wᵢ is a nonnegative raw weight. Divide every weight by their positive total before combining the values. These illustrative values are not measurements from a live AI system.

Try this: double all three weights. The normalized weights and total score should stay the same. Then set all weights to zero.

Experiment controls

Component A

Value A 0.80 to 1 · step 0.01Weight A 30 to 10 · step 0.5

Component B

Value B 0.60 to 1 · step 0.01Weight B 20 to 10 · step 0.5

Component C

Value C 0.40 to 1 · step 0.01Weight C 10 to 10 · step 0.5

The default worked example is shown below.

Raw weight sum6

Normalized weight sum1

Aggregate score0.6667Between 0.4 and 0.8

Each bar is the component value multiplied by its normalized weight. Add the contributions to get the score.

Normalize before adding: divide each raw weight by 6. Then S = 0.4 + 0.2 + 0.0667 = 0.6667. Multiplying every raw weight by the same positive factor leaves the result unchanged. Choosing weights expresses a modeling decision; it does not validate what a score means.

Every term in the weighted sum
Component	Value	Raw weight	Normalized weight	Contribution
Component A	0.8	3	0.5	0.4
Component B	0.6	2	0.3333	0.2
Component C	0.4	1	0.1667	0.0667

Turn exploration into understanding

Learn the ideas behind this lab.

Follow a worked example, practice on your own, and return to the original project sources.

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