Weighted function error
Penguin learning project
The project uses 342 complete Palmer penguin records: 240 training-pool and 102 held-out rows. The selected training rows determine feature scaling and model parameters. Source CSV
This project trains weights with softmax, unlike the homepage lab, which uses nearest species averages. Same penguin measurements; different classification methods. Predictions can differ.
Norms and distances · Measure size or difference in a specified space.
Project vectors have two features; the training matrix has n rows and two columns. Mathematical tools may operate on these values when their assumptions apply.
Consider this concept alongside the project; its source assumptions may differ. These observations do not prove a theorem.
Input: 12 × 2; Held-out evaluation: 102 · #58 · 40.6 mm, 193 mm. Known answer: Adélie; Prediction: Adélie · [≈0.7383, ≈0.2444, ≈0.01737].
Used in these source sections: 3.1 A rule based on distance
The measure μ specifies how function errors are weighted when taking an L² norm.
Changing a measure changes how errors at different inputs contribute.
Source formula example
illustration · This finite probability measure illustrates the integral; it does not reproduce the source’s particular measure.
Symbols
- The measure used to weight the function error.
Source formula example
This source example uses its own inputs and assumptions. Editing it does not change the project.
Edit a value or the code, then run the example. This is a finite demonstration, not a proof of the paper’s theorem.
This example’s data flow
Code changed. Reset to restore this example’s data flow.
Initial values in code
errors=[1, 2]weights=[0.25, 0.75]
Calculated here
Result expression: {
errors,
weights,
squaredError: errors.reduce((s, e, i) => s + weights[i] * e * e, 0),
}
Returns
errors- array (2 items): [1, 2]
weights- array (2 items): [0.25, 0.75]
squaredError- 3.25
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
The numerical values are invented. This finite probability measure illustrates the integral; it does not reproduce the source’s particular measure.
Source: Inline notation