> ## Documentation Index
> Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Recall Score (Deprecated)

> Measures how much of the information in the reference is captured in the hypothesis.

<Warning>
  `recall_score` is deprecated. Use [`recall_at_k`](/future-agi/get-started/evaluation/builtin-evals/recall-at-k) instead. When you omit the `k` parameter (or set it to `null`), `recall_at_k` evaluates the full retrieved list, making it behave exactly like `recall_score`.

  For more retrieval metrics, see also:

  * [`precision_at_k`](/future-agi/get-started/evaluation/builtin-evals/precision-at-k) for retrieval precision
  * [`ndcg_at_k`](/future-agi/get-started/evaluation/builtin-evals/ndcg-at-k) for ranking quality
  * [`mrr`](/future-agi/get-started/evaluation/builtin-evals/mrr) for first-hit ranking
  * [`hit_rate`](/future-agi/get-started/evaluation/builtin-evals/hit-rate) for basic retrieval coverage
</Warning>

<CodeGroup>
  ```python Python theme={null}
  result = evaluator.evaluate(
      eval_templates="recall_score",
      inputs={
          "reference": "The Eiffel Tower is a famous landmark in Paris, built in 1889 for the World's Fair. It stands 324 meters tall.",
          "hypothesis": "The Eiffel Tower, located in Paris, was built in 1889 and is 324 meters high."
      },
      model_name="turing_flash"
  )

  print(result.eval_results[0].output)
  print(result.eval_results[0].reason)
  ```

  ```typescript JS/TS theme={null}
  import { Evaluator, Templates } from "@future-agi/ai-evaluation";

  const evaluator = new Evaluator();

  const result = await evaluator.evaluate(
    "recall_score",
    {
      reference: "The Eiffel Tower is a famous landmark in Paris, built in 1889 for the World's Fair. It stands 324 meters tall.",
      hypothesis: "The Eiffel Tower, located in Paris, was built in 1889 and is 324 meters high."
    },
    {
      modelName: "turing_flash",
    }
  );

  console.log(result);
  ```
</CodeGroup>

| **Input** |                    |          |                                                               |
| --------- | ------------------ | -------- | ------------------------------------------------------------- |
|           | **Required Input** | **Type** | **Description**                                               |
|           | `reference`        | `string` | The reference containing the information to be captured.      |
|           | `hypothesis`       | `string` | The content to be evaluated for recall against the reference. |

| **Output** |            |                                                                                                                              |
| ---------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------- |
|            | **Field**  | **Description**                                                                                                              |
|            | **Result** | Returns a score representing the recall of the hypothesis against the reference, where higher values indicate better recall. |
|            | **Reason** | Provides a detailed explanation of the recall evaluation.                                                                    |

***

### Overview

Recall Score measures how completely a hypothesis text captures the information present in a reference text. Unlike metrics that focus on exact wording, Recall Score evaluates whether the essential information is preserved, regardless of how it's phrased.

A high recall score indicates that the hypothesis contains most or all of the information from the reference, while a low score suggests significant information has been omitted.

***

### What to do If you get Undesired Results

If the recall score is lower than expected:

* Ensure that all key facts, entities, and relationships from the reference are included in the hypothesis
* Check for missing details, numbers, dates, or proper nouns that might be important
* Verify that important contextual information isn't omitted
* Consider that paraphrasing may preserve recall as long as the core information is included
* For summaries, focus on including the most critical information from the reference
* Be aware that recall doesn't penalize for additional information in the hypothesis (that's measured by precision)

***

### Comparing Recall Score with Similar Evals

* [**ROUGE Score**](/future-agi/get-started/evaluation/builtin-evals/rouge): While Recall Score focuses on information coverage, ROUGE Score uses n-gram overlap to evaluate text similarity.
* [**BLEU Score**](/future-agi/get-started/evaluation/builtin-evals/bleu): Recall Score measures how much reference information is captured, while BLEU Score emphasizes precision by measuring how much of the hypothesis matches the reference.
* [**Completeness**](/future-agi/get-started/evaluation/builtin-evals/completeness): Recall Score measures information coverage from a reference text, whereas Completeness evaluates whether a response fully answers a given query.