# `MlBucketInfluencer` [interface-MlBucketInfluencer]

| Name | Type | Description |
| - | - | - |
| `anomaly_score` | [double](./double.md) | A normalized score between 0-100, which is calculated for each bucket influencer. This score might be updated as newer data is analyzed. |
| `bucket_span` | [DurationValue](./DurationValue.md)<[UnitSeconds](./UnitSeconds.md)> | The length of the bucket in seconds. This value matches the bucket span that is specified in the job. |
| `influencer_field_name` | [Field](./Field.md) | The field name of the influencer. |
| `initial_anomaly_score` | [double](./double.md) | The score between 0-100 for each bucket influencer. This score is the initial value that was calculated at the time the bucket was processed. |
| `is_interim` | boolean | If true, this is an interim result. In other words, the results are calculated based on partial input data. |
| `job_id` | [Id](./Id.md) | Identifier for the anomaly detection job. |
| `probability` | [double](./double.md) | The probability that the bucket has this behavior, in the range 0 to 1. This value can be held to a high precision of over 300 decimal places, so the `anomaly_score` is provided as a human-readable and friendly interpretation of this. |
| `raw_anomaly_score` | [double](./double.md) | Internal. |
| `result_type` | string | Internal. This value is always set to `bucket_influencer`. |
| `timestamp_string` | [DateTime](./DateTime.md) | The start time of the bucket for which these results were calculated. |
| `timestamp` | [EpochTime](./EpochTime.md)<[UnitMillis](./UnitMillis.md)> | The start time of the bucket for which these results were calculated. |