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โ€Ždocs/latest/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zipโ€Ž

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โ€Ždocs/latest/_downloads/6a98626692e1880e5e2663974edc45be/plot_02_overview_skore_ui.zipโ€Ž

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โ€Ždocs/latest/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zipโ€Ž

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โ€Ždocs/latest/_downloads/97b4b84f5410e1134b3cbe5e4682d2a1/plot_01_getting_started.zipโ€Ž

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โ€Ždocs/latest/_downloads/dafe7022153ade7f6466ced5b7bc6a6d/plot_03_cross_validate.zipโ€Ž

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โ€Ždocs/latest/_sources/auto_examples/plot_01_getting_started.rst.txtโ€Ž

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@@ -792,7 +792,7 @@ project, such as grid search or cross-validation results.
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** (0 minutes 6.140 seconds)
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**Total running time of the script:** (0 minutes 7.155 seconds)
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.. _sphx_glr_download_auto_examples_plot_01_getting_started.py:

โ€Ždocs/latest/_sources/auto_examples/plot_02_overview_skore_ui.rst.txtโ€Ž

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.. code-block:: none
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array([[-0.45279793, 0.55451844, -1.06675894],
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[ 0.50826404, 1.89978163, 0.8085373 ],
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[-1.2010927 , 0.03846556, 0.00782061]])
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array([[-1.31226173, -0.51377242, 1.43696895],
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[ 0.59008701, 0.27721509, 0.87085756],
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[ 0.2923231 , -0.70647644, -1.37546362]])
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@@ -513,43 +513,43 @@ Pandas data frame:
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<tbody>
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<tr>
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<th>0</th>
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<td>0.181988</td>
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<td>-0.296962</td>
518-
<td>0.250592</td>
519-
<td>-0.141918</td>
520-
<td>0.163869</td>
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<td>1.758881</td>
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<td>-0.232958</td>
518+
<td>-0.034090</td>
519+
<td>-0.181024</td>
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<td>-0.020530</td>
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</tr>
522522
<tr>
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<th>1</th>
524-
<td>-0.719715</td>
525-
<td>0.924959</td>
526-
<td>1.685182</td>
527-
<td>-0.749829</td>
528-
<td>0.798433</td>
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<td>0.709874</td>
525+
<td>0.376033</td>
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<td>-0.191165</td>
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<td>-1.283826</td>
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<td>0.274595</td>
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</tr>
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<tr>
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<th>2</th>
532-
<td>-1.283646</td>
533-
<td>0.149526</td>
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<td>0.025373</td>
535-
<td>0.417901</td>
536-
<td>-0.141121</td>
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<td>0.206991</td>
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<td>-1.579865</td>
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<td>-0.163668</td>
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<td>-1.728073</td>
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<td>-0.147436</td>
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</tr>
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<tr>
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<th>3</th>
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<td>0.585707</td>
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<td>0.325785</td>
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<td>1.533222</td>
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<td>-0.964006</td>
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<td>-0.397607</td>
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<td>0.379173</td>
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<td>-1.487810</td>
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<td>0.354293</td>
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<td>-0.256288</td>
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<td>-0.350852</td>
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</tr>
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<tr>
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<th>4</th>
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<td>-0.163528</td>
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<td>0.216870</td>
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<td>-0.873101</td>
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<td>1.187909</td>
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<td>0.994774</td>
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<td>-0.397813</td>
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<td>-0.580413</td>
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<td>-1.842860</td>
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<td>-0.488077</td>
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<td>-1.153653</td>
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</tbody>
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</table>
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.. _sphx_glr_download_auto_examples_plot_02_overview_skore_ui.py:

โ€Ždocs/latest/_sources/auto_examples/plot_03_cross_validate.rst.txtโ€Ž

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โ€Ždocs/latest/auto_examples/plot_01_getting_started.htmlโ€Ž

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<li><p>In the skore UI on the right, you can drag-and-drop items to re-order them, remove items, etc.</p></li>
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</ol>
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<img alt="Getting started with ``skore`` demo" src="https://raw.githubusercontent.com/sylvaincom/sylvaincom.github.io/master/files/probabl/skore/2024_10_30_skore_demo.gif" />
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.140 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.155 seconds)</p>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-01-getting-started-py">
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<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
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<p><a class="reference download internal" download="" href="../_downloads/e97c5b0c37bdd66afd2a201f006613dd/plot_01_getting_started.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_01_getting_started.ipynb</span></code></a></p>

โ€Ždocs/latest/auto_examples/plot_02_overview_skore_ui.htmlโ€Ž

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<span class="n">my_arr</span>
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</pre></div>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([[-0.45279793, 0.55451844, -1.06675894],
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[ 0.50826404, 1.89978163, 0.8085373 ],
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[-1.2010927 , 0.03846556, 0.00782061]])
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([[-1.31226173, -0.51377242, 1.43696895],
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[ 0.59008701, 0.27721509, 0.87085756],
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[ 0.2923231 , -0.70647644, -1.37546362]])
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<td>0.181988</td>
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<td>-0.296962</td>
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<td>0.250592</td>
670-
<td>-0.141918</td>
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<td>0.163869</td>
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<td>1.758881</td>
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<td>-0.232958</td>
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<td>-0.034090</td>
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<td>-0.181024</td>
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<td>-0.020530</td>
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<th>1</th>
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<td>-0.719715</td>
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<td>0.924959</td>
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<td>1.685182</td>
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<td>-0.749829</td>
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<td>0.798433</td>
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<td>0.709874</td>
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<td>0.376033</td>
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<td>-0.191165</td>
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<td>-1.283826</td>
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<td>0.274595</td>
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<th>2</th>
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<td>-1.283646</td>
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<td>0.149526</td>
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<td>0.025373</td>
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<td>0.417901</td>
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<td>-0.141121</td>
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<td>0.206991</td>
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<td>-1.579865</td>
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<td>-0.163668</td>
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<td>-1.728073</td>
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<td>-0.147436</td>
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<tr>
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<th>3</th>
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<td>0.585707</td>
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<td>0.325785</td>
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<td>1.533222</td>
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<td>-0.964006</td>
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<td>-0.397607</td>
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<td>0.379173</td>
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<td>-1.487810</td>
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<td>0.354293</td>
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<td>-0.256288</td>
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<td>-0.350852</td>
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</tr>
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<th>4</th>
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<td>-0.163528</td>
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<td>0.216870</td>
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<td>-0.873101</td>
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<td>1.187909</td>
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<td>0.994774</td>
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<td>-0.397813</td>
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<td>-0.580413</td>
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<td>-1.842860</td>
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<td>-0.488077</td>
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<td>-1.153653</td>
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</tr>
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</tbody>
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</table>
@@ -2051,7 +2051,7 @@ <h2>Storing scikit-learn models and pipelines<a class="headerlink" href="#storin
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(&#x27;lasso&#x27;, Lasso(alpha=2))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-19" type="checkbox" ><label for="sk-estimator-id-19" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;StandardScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-20" type="checkbox" ><label for="sk-estimator-id-20" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;Lasso<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Lasso.html">?<span>Documentation for Lasso</span></a></label><div class="sk-toggleable__content fitted"><pre>Lasso(alpha=2)</pre></div> </div></div></div></div></div></div>
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<br />
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<br /><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.379 seconds)</p>
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<br /><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.450 seconds)</p>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-02-overview-skore-ui-py">
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<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
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<p><a class="reference download internal" download="" href="../_downloads/e7cbd7f6fc2c2626a6c20bf80bc30ea7/plot_02_overview_skore_ui.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_02_overview_skore_ui.ipynb</span></code></a></p>

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