Jev vs. ML

# Jev vs. classical machine learning

**[Interactive report](https://quicqdev.github.io/Jev-vs-ML/)** · [Completed notebook](notebooks/jev_benchmark_v3.ipynb) · [Results and limitations](docs/results.md)

A comparison of **Jev 1.13.0 and 11 classical classification pipelines** across eight datasets, using three training seeds and a shared test set. The published run uses protocol **3.0.1 (V3)**.

The [V4 additional tests](docs/v4.md) are in development: new policy, temporal, and iterative-decision experiments responding to Reddit feedback, with separate Jev/local notebooks. V4 does not repeat the completed benchmark. The results below and the public report remain V3.

Jev's strongest result is IMDb sentiment classification: **96.3% raw balanced accuracy**, compared with **88.4%** for the best classical pipeline in this run. Results elsewhere are mixed; classical pipelines lead on all four tabular datasets. This is a bounded-budget comparison.

## Start here

| If you want to… | Open |
|---|---|
| Explore the scores | [Interactive report](https://quicqdev.github.io/Jev-vs-ML/) |
| Read the findings and caveats | [Results and interpretation](docs/results.md) |
| Inspect the executed run | [V3 notebook](notebooks/jev_benchmark_v3.ipynb) |
| Download the tables | [Raw CSV](published_results/raw_balanced_accuracy.csv) · [Adjusted CSV](published_results/adjusted_balanced_accuracy.csv) |
| Understand the methodology | [V3 protocol](docs/protocols/BENCHMARK_V3.md) · [Common V2 protocol](docs/protocols/BENCHMARK_V2.md) |
| Check provenance | [Manifest and hashes](published_results/manifest.json) · [Validation record](docs/validation/VALIDATION_V3.md) |
| Download the frozen source | [V3 release bundle](bundles/jev_benchmark_v3_bundle.zip) |
| Work on the repo