hello·ai

Classifying with small models · Module C of 6

Making a decision

How do scores become a confident answer, or an honest "not sure"?

From vectors to a label: prototypes, a small learned head, softmax, temperature, blending and per-class thresholds.

Topics in this module

Topics

0/1 done🔒 Unlocks at 4 of 4 topics in Phase 1 — 0 done.
  1. C1

    Prototypes Planned

    Average a class’s example texts into one vector; descriptions act as weights.

  2. C2

    The classifier head Planned

    Logistic regression on frozen vectors, with learned weights per class.

  3. C3

    Softmax and temperature

    6 minGuided

    Softmax turns scores into probabilities that sum to one; temperature decides how sure those probabilities sound, and calibration makes them honest.

  4. C4

    Combining two scorers Planned

    Blend calibrated probabilities; the blend weight shifts as labelled data grows.

  5. C5

    Thresholds Planned

    Per-class confidence bars decide whether to answer at all.

  6. C6

    Frozen encoder vs fine-tuning Planned

    Train the smallest part that works.