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Delx Log-Loss Calculator
x402
Delx Log-Loss Calculator is a paid API for AI agents from api.delx.ai, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-15).
Computes the log-loss (cross-entropy loss) for a single binary prediction given a predicted probability and an actual binary outcome.
Price / call$0.001
Success—
StatusUnknown
How this service works
Log loss (cross-entropy) for a probability vs binary outcome. Call when training-style forecast quality when sharp mistakes matter. Returns log loss value as deterministic JSON for $0.001 USDC via x402 on Base. First-party local math only—no RPC, live market feed, API keys, storage, or mediagen. Advisory only; the caller owns capital, risk, and production controls.
Output
Returns a JSON object containing the input probability, the input outcome, the schema identifier ('delx/util-log-loss/v1'), and the computed log-loss value as a floating-point number (e.g. 0.3566749439387324).
Example use cases
Classifier confidence audit
“My model predicted a probability of 0.85 that a customer would churn, but the customer actually did churn (outcome 1). What's the log-loss for that prediction?”
Bad prediction penalty check
“I predicted a 0.05 probability for an event and it actually happened (outcome 1). Can you calculate the log-loss so I can see how badly the model was penalized?”
Model calibration spot-check
“I want to spot-check my model's calibration — it said 0.6 probability for a positive class and the true label was 0. What log-loss does that give me?”
Technical details
Endpoint
POSThttps://api.delx.ai/api/v1/x402/log-lossWhen to prefer
Use this endpoint when you need a quick, stateless, per-prediction log-loss calculation without spinning up a local ML library or runtime. Ideal for agent workflows that evaluate individual classifier outputs on-the-fly, audit model confidence scores, or spot-check calibration during inference pipelines. Prefer this over batch evaluation tools when you only need a single data point scored.
Side effects
- Invalid probability value (outside 0–1 range) may cause computation errors or undefined results
- Non-integer outcome value may cause schema validation failure
- Missing required fields (prob or outcome) will return an error
- Probability of exactly 0 or 1 paired with the opposite outcome produces infinite log-loss
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| prob | number | Input field: prob. |
| outcome | integer | Input field: outcome. |
Output
Schema · Response
{
"type": "json",
"example": {
"prob": 0.7,
"schema": "delx/util-log-loss/v1",
"outcome": 1,
"log_loss": 0.3566749439387324
}
}Tags
x402
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