Random feature probability measure
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.
Probability and expectation · Describe random variables and their averages under a law.
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: 4.2 Approximating with random pieces
The source defines a probability measure μg associated with a function g. A probability measure assigns nonnegative weights summing to one in a finite example.
The measure determines the expectation in the feature construction.
Source formula example
illustration · These finite weights illustrate a probability measure; they are not derived from a specified Fourier transform of g.
Symbols
- A probability measure associated with g.
- The function being approximated.
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
weights=[0.25, 0.75]
Calculated here
Result expression: { weights, total: weights.reduce((a, b) => a + b, 0) }
Returns
weights- array (2 items): [0.25, 0.75]
total- 1
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
The numerical values are invented. These finite weights illustrate a probability measure; they are not derived from a specified Fourier transform of g.
Source: Inline notation