Neighborhood Vote

DJ shuwen’s playlist — 24 songs: 16 to train on, 8 to test. The model is always ready: drag new songs in and let the neighbors vote.
PART A · BUILD THE MODEL  data → train (by remembering) → test

Zone 1 · The Data

24 songs, each with two features (tempo, length) and one label. The designer split them up front: 16 for training, 8 for testing — the test songs never enter the machine’s memory.
#SongTempoLengthLabelRolePrediction

Zone 2 · The Model

Training = remembering the 16 train songs — it finished the moment the page opened; nothing else was computed. That is why there is no train button. The painted background shows the model’s decision boundary. The 8 test songs are NOT on this map — they stay locked in the table until the test runs.
DANCE CHILL the decision boundary

Zone 3 · Model Testing

Training error: —
Test error: —
The 8 test songs are scored by the 16-song memory only — a test song is never among its own voters. A new song, not one it memorized.
PART B · USE THE MODEL  apply it to songs it has never met

Zone 4 · Model Application

The same model, drawn again right here — so the feature space is at your fingertips when you drag.
Drag me onto THIS map. Where you drop me is my tempo and my length — my two features. A drop is a prediction, not data: nothing gets recorded.