The Machine: ML transparency and live inference

Three features that expose the tyre-degradation ML models a live ONNX-scored simulator, a held-out blind-test scoreboard, and a full evaluation dashboard so the model’s behaviour is auditable, not a black box.

What this pillar does

The Machine is where the XGBoost tyre-degradation models become interactive and auditable. Three features cover the full transparency arc:

• Understand it. Model Metrics shows the evaluation dashboard written at training time: temporal holdout performance, per-cohort baselines, conformal calibration intervals, and feature importance from SHAP and permutation.
• Test it. Blind-Test Scoreboard scores the model on a held-out season it never saw during training the hardest honest test of whether degradation predictions generalise.
• Play with it. Degradation Simulator lets you dial compound, fuel load, and conditions and watch the ONNX-exported XGBoost quantile regressors score a full stint live in your browser, with cliff probability and stint-life estimates updating in under 5 ms.

The models are documented in full in the ML Pipeline and Validation & Trust sections.

Every feature in this pillar

Degradation Simulator

Dial compound, fuel, and conditions to watch XGBoost models predict pace loss, cliff risk, and tyre life scored live in your browser via ONNX.

Model Metrics

The evaluation dashboard for the tyre-degradation models temporal holdout, per-cohort baselines, conformal calibration, and SHAP-vs-permutation feature importance.

Blind-Test Scoreboard

The degradation models scored on a held-out season predicted-vs-actual scatter, interval coverage, and a cliff-class confusion matrix, all read from the model card.