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md-docs/user_guide/monitoring/drift_explainability.md

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and the production datasets. Variations in a feature's values might suggest that its contribution to the model's predictions
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has changed over time. This entity is available only for tasks with tabular data.
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<figure markdown>
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<figure markdown style="width:100%">
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![Feature Importance](../../imgs/monitoring/drift-explainability/fi.svg)
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<figcaption>Example of a feature importance plot.</figcaption>
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</figure>
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The values represent how strongly a given feature helps to distinguish the datasets, with higher values representing stronger
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separating power. This entity is available only for tasks with tabular data.
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<figure markdown>
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<figure markdown style="width:100%">
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![Variable discriminative power](../../imgs/monitoring/drift-explainability/concept-fi.svg)
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<figcaption>Example of a variable discriminative power plot.</figcaption>
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</figure>

md-docs/user_guide/segment.md

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| Field | Description |
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| --------- | ------- |
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| Name | The name of the Segment. It is used to display information related to the segment in the Web App |
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| Rules | A list of rules defining the subset of the population. These rules are applied in AND between them, which means that a sample belongs to the Segment if all the conditions expressed by the rules are satisfied. To prevent possible conflicts, the rules of a segments need to be applied on different columns. |
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| Name | The name of the Segment. It is used to display information related to the segment in the Web App. |
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| Rules | A list of rules defining the subset of the population. These rules are applied in AND between them, which means that a sample belongs to the Segment if all the conditions expressed by the rules are satisfied. To prevent possible conflicts, there can't be two rules in a segment defined over the same column. |
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Segments can be created both through the Web App and the SDK.
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