Binary classification losses
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.
Individual losses · Give one observed example a loss score.
Project predictions and known answers produce losses. Classification uses cross-entropy and α/2 times squared non-bias weights; Ridge uses normalized-mass MSE and α times all squared coefficients, including the intercept.
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: 1.2 Learning from examples · 1.2.3 Does it work on new examples? · 2.3 The path matters
The 0–1 classification loss takes value 1 for a misclassification and 0 otherwise.
The two values distinguish correct and incorrect outcomes.
Source formula example
illustration · The example uses nonzero ±1 predictions; the source indicator does not count a zero signed margin as negative.
Symbols
- No classification error.
- A classification 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
labels=[1, -1]predictions=[1, 1]
Calculated here
Result expression: { labels, predictions, losses }
losses=labels.map((y, i) => (y * predictions[i] < 0 ? 1 : 0))Uses:labels,predictions
Returns
labels- array (2 items): [1, -1]
predictions- array (2 items): [1, 1]
losses- array (2 items): [0, 1]
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
The numerical values are invented. The example uses nonzero ±1 predictions; the source indicator does not count a zero signed margin as negative.
Source: Inline notation