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Active learning docs update #70

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18 changes: 17 additions & 1 deletion docs/docs/dev/active_learning.md
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Expand Up @@ -6,13 +6,29 @@ Label Sleuth guides the users through the labeling process by suggesting a ranke

Label Sleuth currently supports the following active learning strategies:

::::{tab-set}
:::{tab-item} Binary mode

The employed active learning strategy can be configured by setting the value of the `binary_flow.active_learning_strategy` parameter in the system's [configuration file](configuration.md).

| Active learning strategy | Description |
|---|---|
| `RANDOM` | Randomly sample from unlabeled data (active learning baseline) |
| `HARD_MINING` <br /><defvalue>default</defvalue> | Uncertainty sampling/least confidence ([Lewis and Gale 1994](https://arxiv.org/abs/cmp-lg/9407020)) |
| `RETROSPECTIVE` | Select top scored instances by the model ([Ein-Dor et al. 2019](https://arxiv.org/abs/1911.10763)) |
| `COMBINED_RETROSPECTIVE_HM` | a combination between the previous two strategies |

:::
:::{tab-item} Multiclass mode

The employed active learning strategy can be configured by setting the value of the `multiclass_flow.active_learning_strategy` parameter in the system's [configuration file](configuration.md).

| Active learning strategy | Description |
|---|---|
| `MULTICLASS_ENTROPY` <br /><defvalue>default</defvalue> | Uncertainty sampling/least confidence ([Lewis and Gale 1994](https://arxiv.org/abs/cmp-lg/9407020)) |

The employed active learning strategy can be configured by setting the value of the `active_learning_strategy` parameter in the system's [configuration file](configuration.md).
:::
::::

## Implement a new active learning strategy

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