Random sample pairs
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.
Observations and sampling · Describe examples, targets and samples.
The selected rows supply measurements and known answers to training; held-out IDs remain separate.
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
S is a sequence of m random input-target pairs. Each pair has an input X⁽ⁱ⁾ and target Y⁽ⁱ⁾.
The joint sample is the input to a learning method.
Source formula example
illustration · This shows one possible realized sample; it does not specify its probability law.
Symbols
- A random sample of m pairs.
- Random input and target in pair i.
- Sample size.
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
S=[ [[1, 2], 3], [[0, 1], 2], ]
Calculated here
Result expression: { S, m: S.length }
Returns
S- array (2 items): [array (2 items): [array (2 items), 3], array (2 items): [array (2 items), 2]]
m- 2
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
The numerical values are invented. This shows one possible realized sample; it does not specify its probability law.
Source: Inline notation