Zero target parameter
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.
Source assumptions and notation · Interpret symbols and assumptions in a source statement.
The project supplies finite observations and fitted parameters. A held-out sample does not establish population bounds or prove a theorem.
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: 2.4 A second descent
The condition θ* = 0 sets every true parameter to zero.
This simplifies the regression setting to isolate the noise contribution.
Source formula example
illustration · The example has two parameters; the source permits other dimensions.
Symbols
- The true parameter vector.
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
thetaStar=[0, 0]x=[2, 3]
Calculated here
Result expression: { thetaStar, signal: thetaStar.reduce((s, v, i) => s + v * x[i], 0) }
Returns
thetaStar- array (2 items): [0, 0]
signal- 0
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
The numerical values are invented. The example has two parameters; the source permits other dimensions.
Source: Inline notation