Classify

# Labels and scores

Every verdict has a `label` to act on and the `score` behind it. It also names the `model` that answered.

JSON

```json
{
  "label": "clean",
  "score": 0.04,
  "model": "spam-standard"
}
```

## Labels

- Spam Score 0.8 and above
  The message reads like spam. Drop it, or file it away.
- Clean Score below 0.8
  Deliver it as usual, or hold it for a person if its score is high enough for your own review queue.

Act on the label by default: `if (label === "spam")`. The label is Botpoison's recommended cut on the score, `spam` from 0.8 and `clean` below it, so the two never disagree. The dashboard counts and charts labels, so your form and your dashboard agree.

## Scores

The score is a number from 0 to 1 that ranks how spam-like the submission is. It is not a probability. Each value stands for a false positive rate: the share of real form submissions in Botpoison's reference set, counted together, that score at or above it.

### How often a real message scores this high

The share of messages from real people at or above each score.

| Score | Real messages |
| --- | --- |
| Below 0.2 | about 90% |
| 0.2 and up | 10% |
| 0.3 and up | 4.2% |
| 0.4 and up | 2% |
| 0.5 and up | 1% |
| 0.6 and up | 0.5% |
| 0.65 and up | 0.36% |
| 0.7 and up | 0.25% |
| 0.75 and up | 0.17% |
| 0.8 and up | 0.1% |
| Above 0.8 | under 0.1% |

Every score from 0.8 up is `spam`, so the table does not split those further. A higher score still ranks one message above another.

Every model release is fitted to this table, and a release ships only after a check on a separate set of real messages keeps its rates within twice the table at 0.3, 0.5, 0.65 and 0.8. So `if (score >= 0.7)` means about the same rate after a model moves to a newer release. The [models page](https://botpoison.com/documentation/classify/models/) lists the release each model answers with today.

The rates count every real message in the reference set together, so sites with a lot of traffic weigh the most. Taken site by site, the share of real messages that reach a score is usually several times the table's rate, and higher still on a new site. The rates are measured on whole forms. A plain string sent as `content` is scored on the same scale, but its rates vary more around the table.

## Choosing a threshold

Start with the label. Move to a number of your own only for a reason, and log the score beside what you did with the message while you tune it.

- Default: 0.8, which is the `spam` label. 0.1% of real messages score that high. Drop these, or file them away.
- Review queue: from 0.5 (1% of real messages) or 0.65 (0.36%). Hold these for a person rather than discarding them.

JavaScript

```javascript
const { label, score } = await botpoison.classify(req.body, {
  model: "spam-standard",
});
// Keep the score beside your own outcome while you tune a threshold
log.info({ label, score });
if (label === "spam") {
  // 0.8 and above: 0.1% of real messages score this high
  return res.status(202).end();
}
if (score >= 0.65) {
  // Your own review queue: 0.36% of real messages score this high
  return holdForReview(req.body);
}
// Deliver it as usual
```

## Forms with nothing to read

Some forms carry no free text at all, such as an email-only signup. The model has nothing to read in them, so Classify answers `clean` with a score of 0, unless the model is very confident: a score of 0.8 or more stays `spam` with its score. A review queue at 0.5 never holds a signup for having nothing in it.

A form has free text when it holds any of these:

- A message or subject field, or a field whose name says nothing and whose value runs to five words or more.
- Any word in a name, company, address or other field.
- A link in any field other than email and website.
- An email field that holds anything but one address, or a phone field that holds anything but a phone number.

A string sent as `content` is a message, so this never applies to it.

To see each label without real traffic, use [test mode](https://botpoison.com/documentation/classify/test-mode/).