16 lines
1.6 KiB
Markdown
16 lines
1.6 KiB
Markdown
# `MlBucketInfluencer` [interface-MlBucketInfluencer]
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| Name | Type | Description |
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| - | - | - |
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| `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. |
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| `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. |
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| `influencer_field_name` | [Field](./Field.md) | The field name of the influencer. |
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| `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. |
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| `is_interim` | boolean | If true, this is an interim result. In other words, the results are calculated based on partial input data. |
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| `job_id` | [Id](./Id.md) | Identifier for the anomaly detection job. |
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| `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. |
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| `raw_anomaly_score` | [double](./double.md) | Internal. |
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| `result_type` | string | Internal. This value is always set to `bucket_influencer`. |
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| `timestamp_string` | [DateTime](./DateTime.md) | The start time of the bucket for which these results were calculated. |
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| `timestamp` | [EpochTime](./EpochTime.md)<[UnitMillis](./UnitMillis.md)> | The start time of the bucket for which these results were calculated. |
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