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Tutorials
=========

A collection of tutorials with runnable code demonstrating how to use TabICL.

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    <div class="sphx-glr-thumbcontainer" tooltip="TabICL comes with a fast approximations of SHAP values. It is much faster than using black-box shape routines on TabICL which is slow.">

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  :doc:`/tutorials/interpretability`

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      <div class="sphx-glr-thumbnail-title">Model interpretability with TabICL</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Time series forecasting">

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  :doc:`/tutorials/time_series_forecasting`

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      <div class="sphx-glr-thumbnail-title">Time series forecasting</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how skrub can be used to preprocess datasets with strings or dates for TabICL to make better predictions.">

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  :doc:`/tutorials/string_handling`

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      <div class="sphx-glr-thumbnail-title">Handling strings and dates with skrub</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows the TabICL predicted quantiles on a simple 1D regression problem with heteroscedastic noise.">

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  .. image:: /tutorials/images/thumb/sphx_glr_regression_heteroscedastic_1D_thumb.png
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  :doc:`/tutorials/regression_heteroscedastic_1D`

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      <div class="sphx-glr-thumbnail-title">Quantile regression with TabICL</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the basic usage of TabICL for classification and regression tasks using the scikit-learn compatible API.">

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  :doc:`/tutorials/getting_started`

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      <div class="sphx-glr-thumbnail-title">Getting Started with TabICL</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to use TabICL for classification and how to interpret its probabilistic outputs.">

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  :doc:`/tutorials/classification_2D_proba`

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      <div class="sphx-glr-thumbnail-title">Probabilistic classification</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to use TabICL for unsupervised tasks.">

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  :doc:`/tutorials/unsupervised_learning`

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      <div class="sphx-glr-thumbnail-title">Unsupervised learning</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Adapt a pretrained TabICL regressor to a single dataset with tabicl.FinetunedTabICLRegressor (pinball loss on raw quantiles, same objective the pretrained head was fit with).">

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  .. image:: /tutorials/images/thumb/sphx_glr_finetune_regressor_thumb.png
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  :doc:`/tutorials/finetune_regressor`

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      <div class="sphx-glr-thumbnail-title">Fine-tuning TabICL for regression</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Adapt a pretrained TabICL classifier to a single dataset with tabicl.FinetunedTabICLClassifier (cross-entropy on raw logits, same objective the pretrained head was fit with).">

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  .. image:: /tutorials/images/thumb/sphx_glr_finetune_classifier_thumb.png
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  :doc:`/tutorials/finetune_classifier`

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      <div class="sphx-glr-thumbnail-title">Fine-tuning TabICL for classification</div>
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.. toctree::
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   /tutorials/interpretability
   /tutorials/time_series_forecasting
   /tutorials/string_handling
   /tutorials/regression_heteroscedastic_1D
   /tutorials/getting_started
   /tutorials/classification_2D_proba
   /tutorials/unsupervised_learning
   /tutorials/finetune_regressor
   /tutorials/finetune_classifier


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      :download:`Download all examples in Python source code: tutorials_python.zip </tutorials/tutorials_python.zip>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download all examples in Jupyter notebooks: tutorials_jupyter.zip </tutorials/tutorials_jupyter.zip>`


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    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_