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← Model
Decision trees — retrained on new data

TRAIN v0.15

Same configuration as v0.04 (best so far on the headline benchmark), retrained on the newest dataset.
Completed

Definition

Model type
Decision-tree ensemble
Signals used
Market signals
Technical details
max_depth
2
subsample
0.8
n_estimators
120
learning_rate
0.05
min_samples_leaf
10
Random seed
1015
Config hash
b4d309f22e124511
Created
05 Oct 13:00
Completed
05 Oct 13:00
Artifact
models/v0.15/model.json
sha256 c2def9edd7e9d190…

Dataset & compute

Dataset
Solana Launch Dataset v9
3,937 snapshots · 789 launches · sha256 78a0a22f0364
Training runs
1
Compute
TRAIN training server (CPU)
GPU hours
—
no GPU used
CPU time
8.3 s
Provider cost
$0.00
nothing purchased

Benchmarks

held-out test set · change vs v0.14
Spotting crashes (−70% within 1h)
0.945
Spotting survivors (still traded after 1h)
0.946
Spotting 2× runs (within 1h)
0.834
Calling the 1h direction (down / flat / up)
52.0%
Spotting creator dumps (within 1h)
0.842
Spotting crashes (within 6h)
insufficient data
Spotting survivors (6h)
insufficient data
Calling the 6h direction
insufficient data
Spotting survivors (24h)
insufficient data
Spotting migrations (within 24h)
insufficient data

Validation metrics (during training)

collapse_1h
AUC 0.992
n=435
collapse_6h
—
n=0
reach_2x_1h
AUC 0.971
n=435
survival_1h
AUC 0.922
n=435
survival_6h
—
n=0
survival_24h
—
n=0
migration_24h
—
n=0
trajectory_1h
acc 97.2%
n=435
trajectory_6h
—
n=0
creator_exit_1h
AUC 0.790
n=131

Validation launches are separate from training launches and from the locked test set. Benchmarks above are the public numbers.

What this version relies on

held-out validation launches
This version was trained before TRAIN started measuring which signals each model relies on.

For each signal, how much the score drops when that signal is scrambled across launches. TRAIN learns these weights itself from the data; nobody hand-codes them.

Training runs

Attempt 1Succeeded
05 Oct 13:00 · 00:00:04 · 3,502 examples · $0.00

Timeline

  1. 05 Oct 13:00
    TRAIN v0.15 not deployed; v0.09 stays live
    Reason: average score across 5 tests 0.801 vs 0.812 for the live v0.09.
  2. 05 Oct 13:00
    Benchmark completed: TRAIN v0.15
    Crash-spotting score improved 0.911 → 0.930 vs live v0.09.
  3. 05 Oct 13:00
    Benchmark started: TRAIN v0.15
  4. 05 Oct 13:00
    Training completed: TRAIN v0.15
    00:00:04 · trained on 3502 examples · $0 compute cost
  5. 05 Oct 13:00
    Training started: TRAIN v0.15
    Solana Launch Dataset v9 · decision-tree ensemble · TRAIN training server
  6. 05 Oct 13:00
    TRAIN v0.15 queued
    Same configuration as v0.04 (best so far on the headline benchmark), retrained on the newest dataset.