# Label Review Analytics: IQR Outlier Flags + Missing-Value Fill

> Label Review Analytics: IQR Outlier Flags + Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Analyzes label review records (reviewed_records, changed_labels) to flag IQR-based outliers and fill missing values using a chosen imputation method, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-12-180?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/label-review-analytics-iqr-outlier-flags-missing-value-fill-2a2441bb
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_AdUZJdwh-3DjWG_ixiROt

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability label-review-analytics-iqr-outlier-flags-missing-value-fill-2a2441bb -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and fill any missing values using median imputation on my label review data — the field I care about is reviewed_records — here are up to 200 rows with row_id, date, dataset_group, reviewed_records, and changed_labels?

## When to prefer this

Choose this endpoint when you need both IQR-based outlier detection and missing-value imputation on label review data (reviewed_records and changed_labels) in a single low-cost call. It is ideal for ML data preparation workflows where annotation quality needs to be validated and cleaned before model training. Prefer this over general-purpose analytics tools when your data is structured around dataset_group and date-stamped label review counts and you want two reports in one $0.01 call without any external lookups or predictions.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, reviewed_records, changed_labels) cause validation error
- Invalid date format (not YYYY-MM-DD) triggers pattern mismatch error
- Numeric values outside the ±1 trillion range cause schema validation failure
- Empty rows array returns an error due to minItems:1 constraint
- Invalid method enum value (not zero/mean/median) causes rejection
- Invalid field enum value (not reviewed_records/changed_labels) causes rejection

## How this service works

Label review — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied label review records (reviewed_records, changed_labels). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Returns two independent analytical reports: (1) an IQR-based outlier flag report identifying which rows have values outside the interquartile range for the specified field, and (2) a missing-value fill report showing imputed values for null entries using the chosen method (zero, mean, or median). Both reports cover the same supplied dataset of up to 200 rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "reviewed_records",
     "changed_labels"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "changed_labels": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "reviewed_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "changed_labels": 1,
     "reviewed_records": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "changed_labels": 2,
     "reviewed_records": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "changed_labels": 3,
     "reviewed_records": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "changed_labels": 4,
     "reviewed_records": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "reviewed_records",
    "changed_labels"
   ],
   "type": "string",
   "default": "reviewed_records"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/label-review-analytics-iqr-outlier-flags-missing-value-fill-2a2441bb/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)