Confusion-matrix metrics

Extraits de code

curl "https://api.oanor.com/classifier-api/v1/confusion" \
  -H "x-oanor-key: oanor_test_..."

await fetch("https://api.oanor.com/classifier-api/v1/confusion", {
  headers: { "x-oanor-key": "oanor_test_..." }
});

$ch = curl_init("https://api.oanor.com/classifier-api/v1/confusion");
curl_setopt($ch, CURLOPT_HTTPHEADER, ["x-oanor-key: oanor_test_..."]);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$out = curl_exec($ch);

import requests
requests.get(
    "https://api.oanor.com/classifier-api/v1/confusion",
    headers={"x-oanor-key": "oanor_test_..."}
)

Exemple de réponse

Une réponse réelle de ce endpoint, capturée lors du dernier contrôle de santé.

{
    "data": {
        "mcc": 0.851064,
        "npv": 0.95,
        "note": "From a binary confusion matrix. MCC (Matthews correlation, −1…1) is robust to class imbalance.",
        "inputs": {
            "fn": 5,
            "fp": 10,
            "tn": 95,
            "tp": 90,
            "total": 200
        },
        "recall": 0.947368,
        "accuracy": 0.925,
        "f1_score": 0.923077,
        "precision": 0.9,
        "prevalence": 0.475,
        "sensitivity": 0.947368,
        "specificity": 0.904762,
        "balanced_accuracy": 0.926065,
        "false_negative_rate": 0.052632,
        "false_positive_rate": 0.095238
    },
    "meta": {
        "timestamp": "2026-06-05T03:09:02.520Z",
        "request_id": "befbf95c-e77f-40fc-b03f-2073cac0138a"
    },
    "status": "ok",
    "message": "Confusion metrics",
    "success": true
}