---
metadata:
  - name: generator
    content: Diplodoc Platform v5.50.3
alternate:
  - https://catboost.ai/docs/en/concepts/python-reference_catboost_get_best_score.md
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt

# get_best_score

<!-- source: en/_includes/work_src/reusage/python__method__get_best_score__desc.md -->
Return the best result for each metric calculated on each validation dataset.
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## Method call format {#call-format}

```
get_best_score()
```

## Type of return value {#output-format}

<!-- source: en/_includes/work_src/reusage/python__method__get_best_score__type__div.md -->
dict

Output format:
```bash
{pool_name_1: {metric_1: value,..., metric_N: value}, ..., pool_name_M: {metric_1: value,..., metric_N: value}
```

For example:
```bash
{'validation': {'Logloss': 0.6085537606941837, 'AUC': 0.0}}
```
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## Usage examples {#usage-examples}

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```python
from catboost import CatBoostClassifier, Pool

train_data = [[0, 3],
              [4, 1],
              [8, 1],
              [9, 1]]

train_labels = [0, 0, 1, 1]

eval_data = [[2, 1],
             [3, 1],
             [9, 0],
             [5, 3]]

eval_labels = [0, 1, 1, 0]

eval_dataset = Pool(eval_data,
                    eval_labels)

model = CatBoostClassifier(learning_rate=0.03,
                           custom_metric=['Logloss',
                                          'AUC:hints=skip_train~false'])

model.fit(train_data,
          train_labels,
          eval_set=eval_dataset,
          verbose=False)

print(model.get_best_score())

```

<!-- source: en/_includes/work_src/reusage-code-examples/get-best-score__note.md -->
{% note info %}

This example illustrates the usage of the method with the [CatBoostClassifier](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier.md) class. The usage with other classes is identical.

{% endnote %}
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