Repository: probabl-ai/skore

Issue:
docs: "Get guidance using the reporter.help() method"
### Which part of the documentation needs improvement?

API, `EstimatorReport`

### Describe the problem found in the documentation

When running:
```python
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
X, y = make_classification(random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
estimator = LogisticRegression().fit(X_train, y_train)

from skore import EstimatorReport
report = EstimatorReport(estimator, X_test=X_test, y_test=y_test)
```

I then ran the following and found the `Get guidance using the reporter.help() method` message confusing (but maybe it's just me with a copy-paste frenzy and the error is quickly fixed, so feel free to close this issue):

<img src="https://github.com/user-attachments/assets/54e34d39-fccb-46ef-8e3b-7a570dd34d16" alt="Capture d’écran 2025-01-15 à 15 21 35" width="500"/>

### Suggested improvement

_No response_

### Additional context

_No response_

Discussion:
github-actions[bot]: <!-- Pytest Coverage Comment: display-backend-coverage -->
<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/README.md"><img alt="Coverage" src="https://img.shields.io/badge/Coverage-92%25-brightgreen.svg" /></a><details><summary>Coverage Report for backend </summary><table><tr><th>File</th><th>Stmts</th><th>Miss</th><th>Cover</th><th>Missing</th></tr><tbody><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/__init__.py">\_\_init\_\_.py</a></td><td>14</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/__main__.py">\_\_main\_\_.py</a></td><td>8</td><td>1</td><td>80%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/__main__.py#L19">19</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/exceptions.py">exceptions.py</a></td><td>3</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/cli</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/__init__.py">\_\_init\_\_.py</a></td><td>5</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/cli.py">cli.py</a></td><td>33</td><td>3</td><td>85%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/cli.py#L104">104</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/cli.py#L111">111</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/cli.py#L117">117</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/color_format.py">color_format.py</a></td><td>43</td><td>3</td><td>90%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/color_format.py#L35-L>40">35&ndash;>40</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/color_format.py#L41-L43">41&ndash;43</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/launch_dashboard.py">launch_dashboard.py</a></td><td>26</td><td>15</td><td>39%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/launch_dashboard.py#L36-L57">36&ndash;57</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/quickstart_command.py">quickstart_command.py</a></td><td>14</td><td>7</td><td>50%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/cli/quickstart_command.py#L37-L51">37&ndash;51</a></td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/persistence</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/__init__.py">\_\_init\_\_.py</a></td><td>0</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/persistence/item</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/__init__.py">\_\_init\_\_.py</a></td><td>59</td><td>4</td><td>91%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/__init__.py#L89">89</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/__init__.py#L100-L103">100&ndash;103</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/altair_chart_item.py">altair_chart_item.py</a></td><td>24</td><td>1</td><td>93%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/altair_chart_item.py#L14">14</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/cross_validation_reporter_item.py">cross_validation_reporter_item.py</a></td><td>105</td><td>12</td><td>88%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/cross_validation_reporter_item.py#L28-L41">28&ndash;41</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/cross_validation_reporter_item.py#L125-L126">125&ndash;126</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/cross_validation_reporter_item.py#L254">254</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/cross_validation_reporter_item.py#L257">257</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/item.py">item.py</a></td><td>41</td><td>13</td><td>68%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/item.py#L90">90</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/item.py#L93">93</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/item.py#L97-L117">97&ndash;117</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/matplotlib_figure_item.py">matplotlib_figure_item.py</a></td><td>30</td><td>1</td><td>94%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/matplotlib_figure_item.py#L18">18</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/media_item.py">media_item.py</a></td><td>24</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/numpy_array_item.py">numpy_array_item.py</a></td><td>22</td><td>1</td><td>92%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/numpy_array_item.py#L14">14</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/pandas_dataframe_item.py">pandas_dataframe_item.py</a></td><td>31</td><td>1</td><td>94%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/pandas_dataframe_item.py#L14">14</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/pandas_series_item.py">pandas_series_item.py</a></td><td>31</td><td>1</td><td>94%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/pandas_series_item.py#L14">14</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/pickle_item.py">pickle_item.py</a></td><td>29</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/pillow_image_item.py">pillow_image_item.py</a></td><td>29</td><td>1</td><td>94%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/pillow_image_item.py#L14">14</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/plotly_figure_item.py">plotly_figure_item.py</a></td><td>25</td><td>1</td><td>93%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/plotly_figure_item.py#L14">14</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/polars_dataframe_item.py">polars_dataframe_item.py</a></td><td>29</td><td>1</td><td>94%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/polars_dataframe_item.py#L14">14</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/polars_series_item.py">polars_series_item.py</a></td><td>24</td><td>1</td><td>93%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/polars_series_item.py#L14">14</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/primitive_item.py">primitive_item.py</a></td><td>25</td><td>2</td><td>91%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/primitive_item.py#L13-L15">13&ndash;15</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/sklearn_base_estimator_item.py">sklearn_base_estimator_item.py</a></td><td>28</td><td>1</td><td>94%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/sklearn_base_estimator_item.py#L14">14</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/skrub_table_report_item.py">skrub_table_report_item.py</a></td><td>10</td><td>1</td><td>86%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/item/skrub_table_report_item.py#L11">11</a></td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/persistence/repository</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/repository/__init__.py">\_\_init\_\_.py</a></td><td>3</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/repository/item_repository.py">item_repository.py</a></td><td>59</td><td>5</td><td>91%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/repository/item_repository.py#L15-L16">15&ndash;16</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/repository/item_repository.py#L202-L203">202&ndash;203</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/repository/item_repository.py#L226">226</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/repository/view_repository.py">view_repository.py</a></td><td>19</td><td>3</td><td>81%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/repository/view_repository.py#L9-L10">9&ndash;10</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/repository/view_repository.py#L76">76</a></td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/persistence/storage</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/storage/__init__.py">\_\_init\_\_.py</a></td><td>4</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/storage/abstract_storage.py">abstract_storage.py</a></td><td>22</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/storage/disk_cache_storage.py">disk_cache_storage.py</a></td><td>33</td><td>1</td><td>95%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/storage/disk_cache_storage.py#L44">44</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/storage/in_memory_storage.py">in_memory_storage.py</a></td><td>20</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/persistence/view</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/view/__init__.py">\_\_init\_\_.py</a></td><td>0</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/persistence/view/view.py">view.py</a></td><td>5</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/project</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/__init__.py">\_\_init\_\_.py</a></td><td>3</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/create.py">create.py</a></td><td>52</td><td>8</td><td>88%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/create.py#L116-L122">116&ndash;122</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/create.py#L132-L133">132&ndash;133</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/create.py#L140-L141">140&ndash;141</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/load.py">load.py</a></td><td>22</td><td>3</td><td>88%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/load.py#L45-L47">45&ndash;47</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/open.py">open.py</a></td><td>14</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/project.py">project.py</a></td><td>43</td><td>2</td><td>95%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/project.py#L12">12</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/project/project.py#L171">171</a></td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/sklearn</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/__init__.py">\_\_init\_\_.py</a></td><td>5</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_base.py">_base.py</a></td><td>140</td><td>4</td><td>97%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_base.py#L91">91</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_base.py#L94">94</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_base.py#L168-L>173">168&ndash;>173</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_base.py#L183-L184">183&ndash;184</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/find_ml_task.py">find_ml_task.py</a></td><td>45</td><td>1</td><td>95%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/find_ml_task.py#L71-L>87">71&ndash;>87</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/find_ml_task.py#L80-L>87">80&ndash;>87</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/find_ml_task.py#L86">86</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/types.py">types.py</a></td><td>2</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/sklearn/_cross_validation</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_cross_validation/__init__.py">\_\_init\_\_.py</a></td><td>6</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_cross_validation/metrics_accessor.py">metrics_accessor.py</a></td><td>163</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_cross_validation/report.py">report.py</a></td><td>87</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/sklearn/_estimator</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/__init__.py">\_\_init\_\_.py</a></td><td>6</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/metrics_accessor.py">metrics_accessor.py</a></td><td>265</td><td>4</td><td>97%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/metrics_accessor.py#L149-L158">149&ndash;158</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/metrics_accessor.py#L183-L>236">183&ndash;>236</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/metrics_accessor.py#L191">191</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/metrics_accessor.py#L434-L>437">434&ndash;>437</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/metrics_accessor.py#L783-L>786">783&ndash;>786</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/report.py">report.py</a></td><td>121</td><td>0</td><td>99%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/report.py#L216-L>222">216&ndash;>222</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/report.py#L224-L>226">224&ndash;>226</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/utils.py">utils.py</a></td><td>11</td><td>11</td><td>0%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_estimator/utils.py#L1-L19">1&ndash;19</a></td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/sklearn/_plot</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_plot/__init__.py">\_\_init\_\_.py</a></td><td>4</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_plot/precision_recall_curve.py">precision_recall_curve.py</a></td><td>121</td><td>1</td><td>98%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_plot/precision_recall_curve.py#L229-L>246">229&ndash;>246</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_plot/precision_recall_curve.py#L317">317</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_plot/prediction_error.py">prediction_error.py</a></td><td>97</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_plot/roc_curve.py">roc_curve.py</a></td><td>128</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/_plot/utils.py">utils.py</a></td><td>88</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/sklearn/cross_validation</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/__init__.py">\_\_init\_\_.py</a></td><td>2</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/cross_validation_helpers.py">cross_validation_helpers.py</a></td><td>47</td><td>4</td><td>90%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/cross_validation_helpers.py#L104-L>136">104&ndash;>136</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/cross_validation_helpers.py#L123-L126">123&ndash;126</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/cross_validation_reporter.py">cross_validation_reporter.py</a></td><td>35</td><td>1</td><td>95%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/cross_validation_reporter.py#L177">177</a></td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/plots</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/plots/__init__.py">\_\_init\_\_.py</a></td><td>0</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/plots/compare_scores_plot.py">compare_scores_plot.py</a></td><td>29</td><td>24</td><td>16%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/plots/compare_scores_plot.py#L10">10</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/plots/compare_scores_plot.py#L28-L116">28&ndash;116</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/plots/timing_plot.py">timing_plot.py</a></td><td>29</td><td>24</td><td>17%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/plots/timing_plot.py#L10">10</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/cross_validation/plots/timing_plot.py#L26-L123">26&ndash;123</a></td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/sklearn/train_test_split</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/__init__.py">\_\_init\_\_.py</a></td><td>0</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/train_test_split.py">train_test_split.py</a></td><td>36</td><td>2</td><td>94%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/train_test_split.py#L16-L17">16&ndash;17</a></td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/__init__.py">\_\_init\_\_.py</a></td><td>8</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/high_class_imbalance_too_few_examples_warning.py">high_class_imbalance_too_few_examples_warning.py</a></td><td>17</td><td>3</td><td>78%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/high_class_imbalance_too_few_examples_warning.py#L16-L18">16&ndash;18</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/high_class_imbalance_too_few_examples_warning.py#L80">80</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/high_class_imbalance_warning.py">high_class_imbalance_warning.py</a></td><td>18</td><td>2</td><td>88%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/high_class_imbalance_warning.py#L16-L18">16&ndash;18</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/random_state_unset_warning.py">random_state_unset_warning.py</a></td><td>11</td><td>1</td><td>87%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/random_state_unset_warning.py#L15">15</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/shuffle_true_warning.py">shuffle_true_warning.py</a></td><td>9</td><td>0</td><td>91%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/shuffle_true_warning.py#L44-L>exit">44&ndash;>exit</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/stratify_is_set_warning.py">stratify_is_set_warning.py</a></td><td>11</td><td>1</td><td>87%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/stratify_is_set_warning.py#L15">15</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/time_based_column_warning.py">time_based_column_warning.py</a></td><td>22</td><td>1</td><td>89%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/time_based_column_warning.py#L17">17</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/time_based_column_warning.py#L69-L>exit">69&ndash;>exit</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/train_test_split_warning.py">train_test_split_warning.py</a></td><td>5</td><td>1</td><td>80%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/sklearn/train_test_split/warning/train_test_split_warning.py#L21">21</a></td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/ui</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/ui/__init__.py">\_\_init\_\_.py</a></td><td>0</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/ui/app.py">app.py</a></td><td>32</td><td>8</td><td>68%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/ui/app.py#L26">26</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/ui/app.py#L55-L60">55&ndash;60</a>, <a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/ui/app.py#L72-L74">72&ndash;74</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/ui/dependencies.py">dependencies.py</a></td><td>7</td><td>1</td><td>86%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/ui/dependencies.py#L12">12</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/ui/project_routes.py">project_routes.py</a></td><td>61</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td colspan="5"><b>venv/lib/python3.12/site-packages/skore/utils</b></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/utils/__init__.py">\_\_init\_\_.py</a></td><td>0</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/utils/_accessor.py">_accessor.py</a></td><td>7</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/utils/_logger.py">_logger.py</a></td><td>21</td><td>4</td><td>84%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/utils/_logger.py#L14-L18">14&ndash;18</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/utils/_patch.py">_patch.py</a></td><td>11</td><td>5</td><td>46%</td><td><a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/utils/_patch.py#L19-L35">19&ndash;35</a></td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/utils/_progress_bar.py">_progress_bar.py</a></td><td>28</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td>&nbsp; &nbsp;<a href="https://github.com/probabl-ai/skore/blob/1a3ed2d4ba349e2d5c14728ffdfd97d812219447/venv/lib/python3.12/site-packages/skore/utils/_show_versions.py">_show_versions.py</a></td><td>31</td><td>0</td><td>100%</td><td>&nbsp;</td></tr><tr><td><b>TOTAL</b></td><td><b>2777</b></td><td><b>196</b></td><td><b>92%</b></td><td>&nbsp;</td></tr></tbody></table></details>

| Tests | Skipped | Failures | Errors | Time |
| ----- | ------- | -------- | -------- | ------------------ |
| 609 | 3 :zzz: | 0 :x: | 0 :fire: | 4m 40s :stopwatch: |


Patch:
diff --git a/examples/model_evaluation/plot_estimator_report.py b/examples/model_evaluation/plot_estimator_report.py
index 044ec1f..8d3eb84 100644
--- a/examples/model_evaluation/plot_estimator_report.py
+++ b/examples/model_evaluation/plot_estimator_report.py
@@ -67,27 +67,27 @@ estimator
 # detect that our estimator is already fitted and will not fit it again.
 from skore import EstimatorReport
 
-reporter = EstimatorReport(
+report = EstimatorReport(
     estimator, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test
 )
-reporter
+report
 
 # %%
 #
-# Once the reporter is created, we get some information regarding the available tools
+# Once the report is created, we get some information regarding the available tools
 # allowing us to get some insights from our specific model on the specific task.
 #
 # You can get a similar information if you call the :meth:`~skore.EstimatorReport.help`
 # method.
-reporter.help()
+report.help()
 
 # %%
 #
 # Be aware that you can access the help for each individual sub-accessor. For instance:
-reporter.metrics.help()
+report.metrics.help()
 
 # %%
-reporter.metrics.plot.help()
+report.metrics.plot.help()
 
 # %%
 #
@@ -102,7 +102,7 @@ reporter.metrics.plot.help()
 import time
 
 start = time.time()
-metric_report = reporter.metrics.report_metrics(pos_label=pos_label)
+metric_report = report.metrics.report_metrics(pos_label=pos_label)
 end = time.time()
 metric_report
 
@@ -114,13 +114,13 @@ print(f"Time taken to compute the metrics: {end - start:.2f} seconds")
 # An interesting feature provided by the :class:`skore.EstimatorReport` is the
 # the caching mechanism. Indeed, when we have a large enough dataset, computing the
 # predictions for a model is not cheap anymore. For instance, on our smallish dataset,
-# it took a couple of seconds to compute the metrics. The reporter will cache the
+# it took a couple of seconds to compute the metrics. The report will cache the
 # predictions and if you are interested in computing a metric again or an alternative
 # metric that requires the same predictions, it will be faster. Let's check by
 # requesting the same metrics report again.
 
 start = time.time()
-metric_report = reporter.metrics.report_metrics(pos_label=pos_label)
+metric_report = report.metrics.report_metrics(pos_label=pos_label)
 end = time.time()
 metric_report
 
@@ -143,7 +143,7 @@ plt.tight_layout()
 # and reload them if available. So for instance, let's compute the log loss.
 
 start = time.time()
-log_loss = reporter.metrics.log_loss()
+log_loss = report.metrics.log_loss()
 end = time.time()
 log_loss
 
@@ -154,10 +154,10 @@ print(f"Time taken to compute the log loss: {end - start:.2f} seconds")
 #
 # We can show that without initial cache, it would have taken more time to compute
 # the log loss.
-reporter.clear_cache()
+report.clear_cache()
 
 start = time.time()
-log_loss = reporter.metrics.log_loss()
+log_loss = report.metrics.log_loss()
 end = time.time()
 log_loss
 
@@ -169,7 +169,7 @@ print(f"Time taken to compute the log loss: {end - start:.2f} seconds")
 # By default, the metrics are computed on the test set. However, if a training set
 # is provided, we can also compute the metrics by specifying the `data_source`
 # parameter.
-reporter.metrics.log_loss(data_source="train")
+report.metrics.log_loss(data_source="train")
 
 # %%
 #
@@ -178,7 +178,7 @@ reporter.metrics.log_loss(data_source="train")
 # a `X` and `y` parameters.
 
 start = time.time()
-metric_report = reporter.metrics.report_metrics(
+metric_report = report.metrics.report_metrics(
     data_source="X_y", X=X_test, y=y_test, pos_label=pos_label
 )
 end = time.time()
@@ -195,7 +195,7 @@ print(f"Time taken to compute the metrics: {end - start:.2f} seconds")
 
 # %%
 start = time.time()
-metric_report = reporter.metrics.report_metrics(
+metric_report = report.metrics.report_metrics(
     data_source="X_y", X=X_test, y=y_test, pos_label=pos_label
 )
 end = time.time()
@@ -245,13 +245,13 @@ amount = rng.integers(low=100, high=1000, size=len(y_test))
 #
 # Let's make sure that a function called the `predict` method and cached the result.
 # We compute the accuracy metric to make sure that the `predict` method is called.
-reporter.metrics.accuracy()
+report.metrics.accuracy()
 
 # %%
 #
 # We can now compute the cost of our operational decision.
 start = time.time()
-cost = reporter.metrics.custom_metric(
+cost = report.metrics.custom_metric(
     metric_function=operational_decision_cost,
     metric_name="Operational Decision Cost",
     response_method="predict",
@@ -266,11 +266,11 @@ print(f"Time taken to compute the cost: {end - start:.2f} seconds")
 # %%
 #
 # Let's now clean the cache and see if it is faster.
-reporter.clear_cache()
+report.clear_cache()
 
 # %%
 start = time.time()
-cost = reporter.metrics.custom_metric(
+cost = report.metrics.custom_metric(
     metric_function=operational_decision_cost,
     metric_name="Operational Decision Cost",
     response_method="predict",
@@ -287,7 +287,7 @@ print(f"Time taken to compute the cost: {end - start:.2f} seconds")
 # We observe that caching is working as expected. It is really handy because it means
 # that you can compute some additional metrics without having to recompute the
 # the predictions.
-reporter.metrics.report_metrics(
+report.metrics.report_metrics(
     scoring=["precision", "recall", operational_decision_cost],
     pos_label=pos_label,
     scoring_kwargs={
@@ -314,7 +314,7 @@ operational_decision_cost_scorer = make_scorer(
     metric_name="Operational Decision Cost",
     amount=amount,
 )
-reporter.metrics.report_metrics(scoring=[f1_scorer, operational_decision_cost_scorer])
+report.metrics.report_metrics(scoring=[f1_scorer, operational_decision_cost_scorer])
 
 # %%
 #
@@ -324,12 +324,12 @@ reporter.metrics.report_metrics(scoring=[f1_scorer, operational_decision_cost_sc
 # The :class:`skore.EstimatorReport` class also provides a plotting interface that
 # allows to plot *defacto* the most common plots. As for the the metrics, we only
 # provide the meaningful set of plots for the provided estimator.
-reporter.metrics.plot.help()
+report.metrics.plot.help()
 
 # %%
 #
 # Let's start by plotting the ROC curve for our binary classification task.
-display = reporter.metrics.plot.roc(pos_label=pos_label)
+display = report.metrics.plot.roc(pos_label=pos_label)
 plt.tight_layout()
 
 # %%
@@ -357,7 +357,7 @@ plt.tight_layout()
 # performance gain we can get.
 start = time.time()
 # we already trigger the computation of the predictions in a previous call
-reporter.metrics.plot.roc(pos_label=pos_label)
+report.metrics.plot.roc(pos_label=pos_label)
 plt.tight_layout()
 end = time.time()
 
@@ -367,11 +367,11 @@ print(f"Time taken to compute the ROC curve: {end - start:.2f} seconds")
 # %%
 #
 # Now, let's clean the cache and check if we get a slowdown.
-reporter.clear_cache()
+report.clear_cache()
 
 # %%
 start = time.time()
-reporter.metrics.plot.roc(pos_label=pos_label)
+report.metrics.plot.roc(pos_label=pos_label)
 plt.tight_layout()
 end = time.time()
 
diff --git a/skore/src/skore/sklearn/_base.py b/skore/src/skore/sklearn/_base.py
index e546207..d62963c 100644
--- a/skore/src/skore/sklearn/_base.py
+++ b/skore/src/skore/sklearn/_base.py
@@ -124,7 +124,7 @@ class _BaseReport(_HelpMixin):
 
     def _create_help_tree(self):
         """Create a rich Tree with the available tools and accessor methods."""
-        tree = Tree("reporter")
+        tree = Tree("report")
 
         # Add accessor methods first
         for accessor_attr, config in self._ACCESSOR_CONFIG.items():
@@ -205,8 +205,8 @@ class _BaseAccessor(_HelpMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features) or None, default=None
@@ -237,8 +237,8 @@ class _BaseAccessor(_HelpMixin):
                 missing_data = "X_test" if is_cluster else "X_test and y_test"
                 raise ValueError(
                     f"No {data_source} data (i.e. {missing_data}) were provided "
-                    f"when creating the reporter. Please provide the {data_source} "
-                    "data either when creating the reporter or by setting data_source "
+                    f"when creating the report. Please provide the {data_source} "
+                    "data either when creating the report or by setting data_source "
                     "to 'X_y' and providing X and y."
                 )
             return self._parent._X_test, self._parent._y_test, None
@@ -251,8 +251,8 @@ class _BaseAccessor(_HelpMixin):
                 missing_data = "X_train" if is_cluster else "X_train and y_train"
                 raise ValueError(
                     f"No {data_source} data (i.e. {missing_data}) were provided "
-                    f"when creating the reporter. Please provide the {data_source} "
-                    "data either when creating the reporter or by setting data_source "
+                    f"when creating the report. Please provide the {data_source} "
+                    "data either when creating the report or by setting data_source "
                     "to 'X_y' and providing X and y."
                 )
             return self._parent._X_train, self._parent._y_train, None
@@ -307,8 +307,8 @@ def _get_cached_response_values(
     data_source : {"test", "train", "X_y"}, default="test"
         The data source to use.
 
-        - "test" : use the test set provided when creating the reporter.
-        - "train" : use the train set provided when creating the reporter.
+        - "test" : use the test set provided when creating the report.
+        - "train" : use the train set provided when creating the report.
         - "X_y" : use the provided `X` and `y` to compute the metric.
 
     data_source_hash : int or None
diff --git a/skore/src/skore/sklearn/_cross_validation/metrics_accessor.py b/skore/src/skore/sklearn/_cross_validation/metrics_accessor.py
index a5aa567..ed47e48 100644
--- a/skore/src/skore/sklearn/_cross_validation/metrics_accessor.py
+++ b/skore/src/skore/sklearn/_cross_validation/metrics_accessor.py
@@ -59,12 +59,12 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         scoring : list of str, callable, or scorer, default=None
             The metrics to report. You can get the possible list of string by calling
-            `reporter.metrics.help()`. When passing a callable, it should take as
+            `report.metrics.help()`. When passing a callable, it should take as
             arguments `y_true`, `y_pred` as the two first arguments. Additional
             arguments can be passed as keyword arguments and will be forwarded with
             `scoring_kwargs`. If the callable API is too restrictive (e.g. need to pass
@@ -96,9 +96,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.report_metrics(
+        >>> report.metrics.report_metrics(
         ...     scoring=["precision", "recall"], pos_label=1, aggregate=["mean", "std"]
         ... )
         Compute metric for each split ...
@@ -192,8 +192,8 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         aggregate : {"mean", "std"} or list of such str, default=None
             Function to aggregate the scores across the cross-validation splits.
@@ -210,9 +210,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.accuracy()
+        >>> report.metrics.accuracy()
         Compute metric for each split ...
         Metric                       Accuracy (↗︎)
         LogisticRegression Split #0       0.94...
@@ -244,8 +244,8 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         average : {"binary","macro", "micro", "weighted", "samples"} or None, \
                 default=None
@@ -290,9 +290,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.precision()
+        >>> report.metrics.precision()
         Compute metric for each split ...
         Metric                      Precision (↗︎)
         Class label                              0         1
@@ -327,8 +327,8 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         average : {"binary","macro", "micro", "weighted", "samples"} or None, \
                 default=None
@@ -374,9 +374,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.recall()
+        >>> report.metrics.recall()
         Compute metric for each split ...
         Metric                      Recall (↗︎)
         Class label                           0        1
@@ -402,8 +402,8 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         aggregate : {"mean", "std"} or list of such str, default=None
             Function to aggregate the scores across the cross-validation splits.
@@ -420,9 +420,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.brier_score()
+        >>> report.metrics.brier_score()
         Compute metric for each split ...
         Metric                       Brier score (↘︎)
         LogisticRegression Split #0          0.04...
@@ -454,8 +454,8 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         average : {"auto", "macro", "micro", "weighted", "samples"}, \
                 default=None
@@ -506,9 +506,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.roc_auc()
+        >>> report.metrics.roc_auc()
         Compute metric for each split ...
         Metric                       ROC AUC (↗︎)
         LogisticRegression Split #0      0.99...
@@ -535,8 +535,8 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         aggregate : {"mean", "std"} or list of such str, default=None
             Function to aggregate the scores across the cross-validation splits.
@@ -553,9 +553,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.log_loss()
+        >>> report.metrics.log_loss()
         Compute metric for each split ...
         Metric                       Log loss (↘︎)
         LogisticRegression Split #0       0.1...
@@ -582,8 +582,8 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         multioutput : {"raw_values", "uniform_average"} or array-like of shape \
                 (n_outputs,), default="raw_values"
@@ -610,9 +610,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_diabetes(return_X_y=True)
         >>> regressor = Ridge()
-        >>> reporter = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.r2()
+        >>> report.metrics.r2()
         Compute metric for each split ...
         Metric           R² (↗︎)
         Ridge Split #0  0.36...
@@ -640,8 +640,8 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         multioutput : {"raw_values", "uniform_average"} or array-like of shape \
                 (n_outputs,), default="raw_values"
@@ -668,9 +668,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_diabetes(return_X_y=True)
         >>> regressor = Ridge()
-        >>> reporter = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.rmse()
+        >>> report.metrics.rmse()
         Compute metric for each split ...
         Metric          RMSE (↘︎)
         Ridge Split #0  59.9...
@@ -722,8 +722,8 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         aggregate : {"mean", "std"} or list of such str, default=None
             Function to aggregate the scores across the cross-validation splits.
@@ -744,9 +744,9 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_diabetes(return_X_y=True)
         >>> regressor = Ridge()
-        >>> reporter = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.metrics.custom_metric(
+        >>> report.metrics.custom_metric(
         ...     metric_function=mean_absolute_error,
         ...     response_method="predict",
         ...     metric_name="MAE (↗︎)",
@@ -832,13 +832,13 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         )
 
     def _get_help_tree_title(self):
-        return "[bold cyan]reporter.metrics[/bold cyan]"
+        return "[bold cyan]report.metrics[/bold cyan]"
 
     def __repr__(self):
         """Return a string representation using rich."""
         return self._rich_repr(
             class_name="skore.CrossValidationReport.metrics",
-            help_method_name="reporter.metrics.help()",
+            help_method_name="report.metrics.help()",
         )
 
 
@@ -873,8 +873,8 @@ class _PlotMetricsAccessor(_BaseAccessor):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         response_method : str
             The response method.
@@ -953,8 +953,8 @@ class _PlotMetricsAccessor(_BaseAccessor):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         pos_label : int, float, bool or str, default=None
             The positive class.
@@ -974,9 +974,9 @@ class _PlotMetricsAccessor(_BaseAccessor):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> display = reporter.metrics.plot.roc()
+        >>> display = report.metrics.plot.roc()
         Computing predictions for display ...
         >>> display.plot(roc_curve_kwargs={"color": "tab:red"})
         """
@@ -1004,8 +1004,8 @@ class _PlotMetricsAccessor(_BaseAccessor):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         pos_label : int, float, bool or str, default=None
             The positive class.
@@ -1025,9 +1025,9 @@ class _PlotMetricsAccessor(_BaseAccessor):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> display = reporter.metrics.plot.precision_recall()
+        >>> display = report.metrics.plot.precision_recall()
         Computing predictions for display ...
         >>> display.plot()
         """
@@ -1061,8 +1061,8 @@ class _PlotMetricsAccessor(_BaseAccessor):
         data_source : {"test", "train"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
 
         ax : matplotlib axes, default=None
             Axes object to plot on. If `None`, a new figure and axes is
@@ -1100,9 +1100,9 @@ class _PlotMetricsAccessor(_BaseAccessor):
         >>> from skore import CrossValidationReport
         >>> X, y = load_diabetes(return_X_y=True)
         >>> regressor = Ridge()
-        >>> reporter = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> display = reporter.metrics.plot.prediction_error(
+        >>> display = report.metrics.plot.prediction_error(
         ...     kind="actual_vs_predicted"
         ... )
         Computing predictions for display ...
@@ -1122,11 +1122,11 @@ class _PlotMetricsAccessor(_BaseAccessor):
         return "[bold cyan]Available plot methods[/bold cyan]"
 
     def _get_help_tree_title(self):
-        return "[bold cyan]reporter.metrics.plot[/bold cyan]"
+        return "[bold cyan]report.metrics.plot[/bold cyan]"
 
     def __repr__(self):
         """Return a string representation using rich."""
         return self._rich_repr(
             class_name="skore.CrossValidationReport.metrics.plot",
-            help_method_name="reporter.metrics.plot.help()",
+            help_method_name="report.metrics.plot.help()",
         )
diff --git a/skore/src/skore/sklearn/_cross_validation/report.py b/skore/src/skore/sklearn/_cross_validation/report.py
index 0ab630c..7e01a56 100644
--- a/skore/src/skore/sklearn/_cross_validation/report.py
+++ b/skore/src/skore/sklearn/_cross_validation/report.py
@@ -26,7 +26,7 @@ def _generate_estimator_report(estimator, X, y, train_indices, test_indices):
 
 
 class CrossValidationReport(_BaseReport, DirNamesMixin):
-    """Reporter for cross-validation results.
+    """Report for cross-validation results.
 
     Parameters
     ----------
@@ -180,14 +180,14 @@ class CrossValidationReport(_BaseReport, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.cache_predictions()
+        >>> report.cache_predictions()
         Cross-validation predictions ...
         Caching predictions ...
         Caching predictions ...
-        >>> reporter.clear_cache()
-        >>> reporter._cache
+        >>> report.clear_cache()
+        >>> report._cache
         {}
         """
         for report in self.estimator_reports_:
@@ -215,13 +215,13 @@ class CrossValidationReport(_BaseReport, DirNamesMixin):
         >>> from skore import CrossValidationReport
         >>> X, y = load_breast_cancer(return_X_y=True)
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
+        >>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
         Processing cross-validation ...
-        >>> reporter.cache_predictions()
+        >>> report.cache_predictions()
         Cross-validation predictions ...
         Caching predictions ...
         Caching predictions ...
-        >>> reporter._cache
+        >>> report._cache
         {...}
         """
         if n_jobs is None:
@@ -300,5 +300,5 @@ class CrossValidationReport(_BaseReport, DirNamesMixin):
     def __repr__(self):
         """Return a string representation using rich."""
         return self._rich_repr(
-            class_name="skore.CrossValidationReport", help_method_name="reporter.help()"
+            class_name="skore.CrossValidationReport", help_method_name="help()"
         )
diff --git a/skore/src/skore/sklearn/_estimator/metrics_accessor.py b/skore/src/skore/sklearn/_estimator/metrics_accessor.py
index 72016b8..b2e5f1f 100644
--- a/skore/src/skore/sklearn/_estimator/metrics_accessor.py
+++ b/skore/src/skore/sklearn/_estimator/metrics_accessor.py
@@ -63,21 +63,21 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         scoring : list of str, callable, or scorer, default=None
             The metrics to report. You can get the possible list of string by calling
-            `reporter.metrics.help()`. When passing a callable, it should take as
+            `report.metrics.help()`. When passing a callable, it should take as
             arguments `y_true`, `y_pred` as the two first arguments. Additional
             arguments can be passed as keyword arguments and will be forwarded with
             `scoring_kwargs`. If the callable API is too restrictive (e.g. need to pass
@@ -109,14 +109,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.report_metrics(pos_label=1)
+        >>> report.metrics.report_metrics(pos_label=1)
         Metric              Precision (↗︎)  Recall (↗︎)  ROC AUC (↗︎)  Brier score (↘︎)
         LogisticRegression        0.98...     0.93...      0.99...          0.03...
         """
@@ -374,17 +374,17 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         Returns
         -------
@@ -401,14 +401,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.accuracy()
+        >>> report.metrics.accuracy()
         Metric              Accuracy (↗︎)
         LogisticRegression       0.95...
         """
@@ -459,17 +459,17 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         average : {"binary","macro", "micro", "weighted", "samples"} or None, \
                 default=None
@@ -514,14 +514,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.precision(pos_label=1)
+        >>> report.metrics.precision(pos_label=1)
         Metric              Precision (↗︎)
         LogisticRegression        0.98...
         """
@@ -588,17 +588,17 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         average : {"binary","macro", "micro", "weighted", "samples"} or None, \
                 default=None
@@ -644,14 +644,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.recall(pos_label=1)
+        >>> report.metrics.recall(pos_label=1)
         Metric              Recall (↗︎)
         LogisticRegression     0.93...
         """
@@ -714,17 +714,17 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         Returns
         -------
@@ -741,14 +741,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.brier_score()
+        >>> report.metrics.brier_score()
         Metric              Brier score (↘︎)
         LogisticRegression          0.03...
         """
@@ -809,17 +809,17 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         average : {"auto", "macro", "micro", "weighted", "samples"}, \
                 default=None
@@ -870,14 +870,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.roc_auc()
+        >>> report.metrics.roc_auc()
         Metric              ROC AUC (↗︎)
         LogisticRegression      0.99...
         """
@@ -936,11 +936,11 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ----------
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         Returns
         -------
@@ -957,14 +957,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.log_loss()
+        >>> report.metrics.log_loss()
         Metric              Log loss (↘︎)
         LogisticRegression       0.10...
         """
@@ -1014,17 +1014,17 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         multioutput : {"raw_values", "uniform_average"} or array-like of shape \
                 (n_outputs,), default="raw_values"
@@ -1051,14 +1051,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_diabetes(return_X_y=True), random_state=0
         ... )
         >>> regressor = Ridge()
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     regressor,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.r2()
+        >>> report.metrics.r2()
         Metric  R² (↗︎)
         Ridge   0.35...
         """
@@ -1111,17 +1111,17 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         multioutput : {"raw_values", "uniform_average"} or array-like of shape \
                 (n_outputs,), default="raw_values"
@@ -1148,14 +1148,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_diabetes(return_X_y=True), random_state=0
         ... )
         >>> regressor = Ridge()
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     regressor,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.rmse()
+        >>> report.metrics.rmse()
         Metric  RMSE (↘︎)
         Ridge   56.5...
         """
@@ -1239,17 +1239,17 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         **kwargs : dict
             Any additional keyword arguments to be passed to the metric function.
@@ -1270,14 +1270,14 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         ...     *load_diabetes(return_X_y=True), random_state=0
         ... )
         >>> regressor = Ridge()
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     regressor,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.metrics.custom_metric(
+        >>> report.metrics.custom_metric(
         ...     metric_function=mean_absolute_error,
         ...     response_method="predict",
         ...     metric_name="MAE (↗︎)",
@@ -1391,13 +1391,13 @@ class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
         )
 
     def _get_help_tree_title(self):
-        return "[bold cyan]reporter.metrics[/bold cyan]"
+        return "[bold cyan]report.metrics[/bold cyan]"
 
     def __repr__(self):
         """Return a string representation using rich."""
         return self._rich_repr(
             class_name="skore.EstimatorReport.metrics",
-            help_method_name="reporter.metrics.help()",
+            help_method_name="report.metrics.help()",
         )
 
 
@@ -1438,8 +1438,8 @@ class _PlotMetricsAccessor(_BaseAccessor):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         response_method : str
@@ -1509,17 +1509,17 @@ class _PlotMetricsAccessor(_BaseAccessor):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         pos_label : int, float, bool or str, default=None
             The positive class.
@@ -1542,14 +1542,14 @@ class _PlotMetricsAccessor(_BaseAccessor):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> display = reporter.metrics.plot.roc()
+        >>> display = report.metrics.plot.roc()
         >>> display.plot(roc_curve_kwargs={"color": "tab:red"})
         """
         response_method = ("predict_proba", "decision_function")
@@ -1586,17 +1586,17 @@ class _PlotMetricsAccessor(_BaseAccessor):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         pos_label : int, float, bool or str, default=None
             The positive class.
@@ -1619,14 +1619,14 @@ class _PlotMetricsAccessor(_BaseAccessor):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> display = reporter.metrics.plot.precision_recall()
+        >>> display = report.metrics.plot.precision_recall()
         >>> display.plot(pr_curve_kwargs={"color": "tab:red"})
         """
         response_method = ("predict_proba", "decision_function")
@@ -1663,17 +1663,17 @@ class _PlotMetricsAccessor(_BaseAccessor):
         data_source : {"test", "train", "X_y"}, default="test"
             The data source to use.
 
-            - "test" : use the test set provided when creating the reporter.
-            - "train" : use the train set provided when creating the reporter.
+            - "test" : use the test set provided when creating the report.
+            - "train" : use the train set provided when creating the report.
             - "X_y" : use the provided `X` and `y` to compute the metric.
 
         X : array-like of shape (n_samples, n_features), default=None
             New data on which to compute the metric. By default, we use the validation
-            set provided when creating the reporter.
+            set provided when creating the report.
 
         y : array-like of shape (n_samples,), default=None
             New target on which to compute the metric. By default, we use the target
-            provided when creating the reporter.
+            provided when creating the report.
 
         ax : matplotlib axes, default=None
             Axes object to plot on. If `None`, a new figure and axes is
@@ -1714,14 +1714,14 @@ class _PlotMetricsAccessor(_BaseAccessor):
         ...     *load_diabetes(return_X_y=True), random_state=0
         ... )
         >>> regressor = Ridge()
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     regressor,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> display = reporter.metrics.plot.prediction_error(
+        >>> display = report.metrics.plot.prediction_error(
         ...     kind="actual_vs_predicted"
         ... )
         >>> display.plot(line_kwargs={"color": "tab:red"})
@@ -1742,11 +1742,11 @@ class _PlotMetricsAccessor(_BaseAccessor):
         return "[bold cyan]Available plot methods[/bold cyan]"
 
     def _get_help_tree_title(self):
-        return "[bold cyan]reporter.metrics.plot[/bold cyan]"
+        return "[bold cyan]report.metrics.plot[/bold cyan]"
 
     def __repr__(self):
         """Return a string representation using rich."""
         return self._rich_repr(
             class_name="skore.EstimatorReport.metrics.plot",
-            help_method_name="reporter.metrics.plot.help()",
+            help_method_name="report.metrics.plot.help()",
         )
diff --git a/skore/src/skore/sklearn/_estimator/report.py b/skore/src/skore/sklearn/_estimator/report.py
index 3254dc4..72ff44f 100644
--- a/skore/src/skore/sklearn/_estimator/report.py
+++ b/skore/src/skore/sklearn/_estimator/report.py
@@ -158,17 +158,17 @@ class EstimatorReport(_BaseReport, DirNamesMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.cache_predictions()
+        >>> report.cache_predictions()
         Caching predictions ...
-        >>> reporter.clear_cache()
-        >>> reporter._cache
+        >>> report.clear_cache()
+        >>> report._cache
         {}
         """
         self._cache = {}
@@ -199,16 +199,16 @@ class EstimatorReport(_BaseReport, DirNamesMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> reporter.cache_predictions()
+        >>> report.cache_predictions()
         Caching predictions ...
-        >>> reporter._cache
+        >>> report._cache
         {...}
         """
         if self._ml_task in ("binary-classification", "multiclass-classification"):
@@ -330,5 +330,5 @@ class EstimatorReport(_BaseReport, DirNamesMixin):
     def __repr__(self):
         """Return a string representation using rich."""
         return self._rich_repr(
-            class_name="skore.EstimatorReport", help_method_name="reporter.help()"
+            class_name="skore.EstimatorReport", help_method_name="help()"
         )
diff --git a/skore/src/skore/sklearn/_plot/precision_recall_curve.py b/skore/src/skore/sklearn/_plot/precision_recall_curve.py
index 96ce28a..a785ef0 100644
--- a/skore/src/skore/sklearn/_plot/precision_recall_curve.py
+++ b/skore/src/skore/sklearn/_plot/precision_recall_curve.py
@@ -90,14 +90,14 @@ class PrecisionRecallCurveDisplay(HelpDisplayMixin, _ClassifierCurveDisplayMixin
     ...     *load_breast_cancer(return_X_y=True), random_state=0
     ... )
     >>> classifier = LogisticRegression(max_iter=10_000)
-    >>> reporter = EstimatorReport(
+    >>> report = EstimatorReport(
     ...     classifier,
     ...     X_train=X_train,
     ...     y_train=y_train,
     ...     X_test=X_test,
     ...     y_test=y_test,
     ... )
-    >>> display = reporter.metrics.plot.precision_recall()
+    >>> display = report.metrics.plot.precision_recall()
     >>> display.plot(pr_curve_kwargs={"color": "tab:red"})
     """
 
@@ -173,14 +173,14 @@ class PrecisionRecallCurveDisplay(HelpDisplayMixin, _ClassifierCurveDisplayMixin
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> display = reporter.metrics.plot.precision_recall()
+        >>> display = report.metrics.plot.precision_recall()
         >>> display.plot(pr_curve_kwargs={"color": "tab:red"})
         """
         self.ax_, self.figure_, estimator_name = self._validate_plot_params(
diff --git a/skore/src/skore/sklearn/_plot/prediction_error.py b/skore/src/skore/sklearn/_plot/prediction_error.py
index d5b9836..dea17a4 100644
--- a/skore/src/skore/sklearn/_plot/prediction_error.py
+++ b/skore/src/skore/sklearn/_plot/prediction_error.py
@@ -67,14 +67,14 @@ class PredictionErrorDisplay(HelpDisplayMixin):
     ...     *load_diabetes(return_X_y=True), random_state=0
     ... )
     >>> classifier = Ridge()
-    >>> reporter = EstimatorReport(
+    >>> report = EstimatorReport(
     ...     classifier,
     ...     X_train=X_train,
     ...     y_train=y_train,
     ...     X_test=X_test,
     ...     y_test=y_test,
     ... )
-    >>> display = reporter.metrics.plot.prediction_error()
+    >>> display = report.metrics.plot.prediction_error()
     >>> display.plot(kind="actual_vs_predicted")
     """
 
@@ -144,14 +144,14 @@ class PredictionErrorDisplay(HelpDisplayMixin):
         ...     *load_diabetes(return_X_y=True), random_state=0
         ... )
         >>> classifier = Ridge()
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> display = reporter.metrics.plot.prediction_error()
+        >>> display = report.metrics.plot.prediction_error()
         >>> display.plot(kind="actual_vs_predicted")
         """
         expected_kind = ("actual_vs_predicted", "residual_vs_predicted")
diff --git a/skore/src/skore/sklearn/_plot/roc_curve.py b/skore/src/skore/sklearn/_plot/roc_curve.py
index 256d44d..577046d 100644
--- a/skore/src/skore/sklearn/_plot/roc_curve.py
+++ b/skore/src/skore/sklearn/_plot/roc_curve.py
@@ -91,14 +91,14 @@ class RocCurveDisplay(HelpDisplayMixin, _ClassifierCurveDisplayMixin):
     ...     *load_breast_cancer(return_X_y=True), random_state=0
     ... )
     >>> classifier = LogisticRegression(max_iter=10_000)
-    >>> reporter = EstimatorReport(
+    >>> report = EstimatorReport(
     ...     classifier,
     ...     X_train=X_train,
     ...     y_train=y_train,
     ...     X_test=X_test,
     ...     y_test=y_test,
     ... )
-    >>> display = reporter.metrics.plot.roc()
+    >>> display = report.metrics.plot.roc()
     >>> display.plot(roc_curve_kwargs={"color": "tab:red"})
     """
 
@@ -172,14 +172,14 @@ class RocCurveDisplay(HelpDisplayMixin, _ClassifierCurveDisplayMixin):
         ...     *load_breast_cancer(return_X_y=True), random_state=0
         ... )
         >>> classifier = LogisticRegression(max_iter=10_000)
-        >>> reporter = EstimatorReport(
+        >>> report = EstimatorReport(
         ...     classifier,
         ...     X_train=X_train,
         ...     y_train=y_train,
         ...     X_test=X_test,
         ...     y_test=y_test,
         ... )
-        >>> display = reporter.metrics.plot.roc()
+        >>> display = report.metrics.plot.roc()
         >>> display.plot(roc_curve_kwargs={"color": "tab:red"})
         """
         self.ax_, self.figure_, estimator_name = self._validate_plot_params(
diff --git a/sphinx/api.rst b/sphinx/api.rst
index 80f3943..45a2707 100644
--- a/sphinx/api.rst
+++ b/sphinx/api.rst
@@ -40,9 +40,9 @@ These functions and classes enhance scikit-learn's ones.
 Report for a single estimator
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The class :class:`EstimatorReport` provides a reporter allowing to inspect and
+The class :class:`EstimatorReport` provides a report allowing to inspect and
 evaluate a scikit-learn estimator in an interactive way. The functionalities of the
-reporter are accessible through accessors.
+report are accessible through accessors.
 
 .. autosummary::
     :toctree: generated/
@@ -95,9 +95,9 @@ performance metric representations.
 Cross-validation report for an estimator
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The class :class:`CrossValidationReport` provides a reporter allowing to inspect and
+The class :class:`CrossValidationReport` provides a report allowing to inspect and
 evaluate a scikit-learn estimator through cross-validation in an interactive way. The
-functionalities of the reporter are accessible through accessors.
+functionalities of the report are accessible through accessors.
 
 .. autosummary::
     :toctree: generated/


Test patch:
diff --git a/skore/tests/unit/sklearn/test_base.py b/skore/tests/unit/sklearn/test_base.py
index 53b4b84..937e4ae 100644
--- a/skore/tests/unit/sklearn/test_base.py
+++ b/skore/tests/unit/sklearn/test_base.py
@@ -178,7 +178,7 @@ def test_get_cached_response_values_different_data_source_hash(
 
 class MockReport:
     def __init__(self, estimator, X_train=None, y_train=None, X_test=None, y_test=None):
-        """Mock a reporter with the minimal required attributes."""
+        """Mock a report with the minimal required attributes."""
         self._estimator = estimator
         self._X_train = X_train
         self._y_train = y_train
@@ -224,8 +224,8 @@ def test_base_accessor_get_X_y_and_data_source_hash_error():
     for data_source in ("train", "test"):
         err_msg = re.escape(
             f"No {data_source} data (i.e. X_{data_source} and y_{data_source}) were "
-            f"provided when creating the reporter. Please provide the {data_source} "
-            "data either when creating the reporter or by setting data_source to "
+            f"provided when creating the report. Please provide the {data_source} "
+            "data either when creating the report or by setting data_source to "
             "'X_y' and providing X and y."
         )
         with pytest.raises(ValueError, match=err_msg):
@@ -261,8 +261,8 @@ def test_base_accessor_get_X_y_and_data_source_hash_error():
     for data_source in ("train", "test"):
         err_msg = re.escape(
             f"No {data_source} data (i.e. X_{data_source}) were provided when "
-            f"creating the reporter. Please provide the {data_source} data either "
-            f"when creating the reporter or by setting data_source to 'X_y' and "
+            f"creating the report. Please provide the {data_source} data either "
+            f"when creating the report or by setting data_source to 'X_y' and "
             f"providing X and y."
         )
         with pytest.raises(ValueError, match=err_msg):
diff --git a/skore/tests/unit/sklearn/test_cross_validation.py b/skore/tests/unit/sklearn/test_cross_validation.py
index 34f8a74..ad845db 100644
--- a/skore/tests/unit/sklearn/test_cross_validation.py
+++ b/skore/tests/unit/sklearn/test_cross_validation.py
@@ -170,7 +170,7 @@ def test_cross_validation_report_repr(binary_classification_data):
 
     repr_str = repr(report)
     assert "skore.CrossValidationReport" in repr_str
-    assert "reporter.help()" in repr_str
+    assert "help()" in repr_str
 
 
 @pytest.mark.parametrize(
@@ -237,7 +237,7 @@ def test_cross_validation_report_plot_repr(binary_classification_data):
 
     repr_str = repr(report.metrics.plot)
     assert "skore.CrossValidationReport.metrics.plot" in repr_str
-    assert "reporter.metrics.plot.help()" in repr_str
+    assert "report.metrics.plot.help()" in repr_str
 
 
 def test_cross_validation_report_plot_roc(binary_classification_data):
@@ -295,7 +295,7 @@ def test_cross_validation_report_metrics_repr(binary_classification_data):
 
     repr_str = repr(report.metrics)
     assert "skore.CrossValidationReport.metrics" in repr_str
-    assert "reporter.metrics.help()" in repr_str
+    assert "report.metrics.help()" in repr_str
 
 
 def _normalize_metric_name(column):
diff --git a/skore/tests/unit/sklearn/test_estimator.py b/skore/tests/unit/sklearn/test_estimator.py
index 77c791b..61c1e35 100644
--- a/skore/tests/unit/sklearn/test_estimator.py
+++ b/skore/tests/unit/sklearn/test_estimator.py
@@ -291,7 +291,7 @@ def test_estimator_report_repr(binary_classification_data):
 
     repr_str = repr(report)
     assert "skore.EstimatorReport" in repr_str
-    assert "reporter.help()" in repr_str
+    assert "help()" in repr_str
 
 
 @pytest.mark.parametrize(
@@ -364,7 +364,7 @@ def test_estimator_report_plot_repr(binary_classification_data):
 
     repr_str = repr(report.metrics.plot)
     assert "skore.EstimatorReport.metrics.plot" in repr_str
-    assert "reporter.metrics.plot.help()" in repr_str
+    assert "report.metrics.plot.help()" in repr_str
 
 
 def test_estimator_report_plot_roc(binary_classification_data):
@@ -504,7 +504,7 @@ def test_estimator_report_metrics_repr(binary_classification_data):
 
     repr_str = repr(report.metrics)
     assert "skore.EstimatorReport.metrics" in repr_str
-    assert "reporter.metrics.help()" in repr_str
+    assert "report.metrics.help()" in repr_str
 
 
 @pytest.mark.parametrize(
@@ -946,7 +946,7 @@ def test_estimator_report_custom_metric_compatible_estimator(
 
     class CompatibleEstimator:
         """Estimator exposing only a predict method but it should be enough for the
-        reporters.
+        reports.
         """
 
         def fit(self, X, y):
@@ -1060,8 +1060,8 @@ def test_estimator_report_get_X_y_and_data_source_hash_error():
     for data_source in ("train", "test"):
         err_msg = re.escape(
             f"No {data_source} data (i.e. X_{data_source} and y_{data_source}) were "
-            f"provided when creating the reporter. Please provide the {data_source} "
-            "data either when creating the reporter or by setting data_source to "
+            f"provided when creating the report. Please provide the {data_source} "
+            "data either when creating the report or by setting data_source to "
             "'X_y' and providing X and y."
         )
         with pytest.raises(ValueError, match=err_msg):
@@ -1094,8 +1094,8 @@ def test_estimator_report_get_X_y_and_data_source_hash_error():
     for data_source in ("train", "test"):
         err_msg = re.escape(
             f"No {data_source} data (i.e. X_{data_source}) were provided when "
-            f"creating the reporter. Please provide the {data_source} data either "
-            f"when creating the reporter or by setting data_source to 'X_y' and "
+            f"creating the report. Please provide the {data_source} data either "
+            f"when creating the report or by setting data_source to 'X_y' and "
             f"providing X and y."
         )
         with pytest.raises(ValueError, match=err_msg):