

.. _sphx_glr_auto_examples_applications:

.. _realworld_examples:

Examples based on real world datasets
-------------------------------------

Applications to real world problems with some medium sized datasets or
interactive user interface.



.. raw:: html

    <div class="sphx-glr-thumbnails">

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in computed tomography (CT).">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_tomography_l1_reconstruction_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_tomography_l1_reconstruction`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is a preprocessed excerpt of the &quot;Labeled Faces in the Wild&quot;, aka LFW: https://www.kaggle.com/datasets/jessicali9530/lfw-dataset">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_face_recognition_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_face_recognition`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Faces recognition example using eigenfaces and SVMs</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use KernelPCA to denoise images. In short, we take advantage of the approximation function learned during fit to reconstruct the original image.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_digits_denoising_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_digits_denoising`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Image denoising using kernel PCA</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how Polars-engineered lagged features can be used for time series forecasting with HistGradientBoostingRegressor on the Bike Sharing Demand dataset.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_time_series_lagged_features_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_time_series_lagged_features`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Lagged features for time series forecasting</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrate how model complexity influences both prediction accuracy and computational performance.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_model_complexity_influence_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_model_complexity_influence`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Model Complexity Influence</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how scikit-learn can be used for classification using an out-of-core approach: learning from data that doesn&#x27;t fit into main memory. We make use of an online classifier, i.e., one that supports the partial_fit method, that will be fed with batches of examples. To guarantee that the features space remains the same over time we leverage a HashingVectorizer that will project each example into the same feature space. This is especially useful in the case of text classification where new features (words) may appear in each batch.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_out_of_core_classification_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_out_of_core_classification`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Out-of-core classification of text documents</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the need for robust covariance estimation on a real data set. It is useful both for outlier detection and for a better understanding of the data structure.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_outlier_detection_wine_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_outlier_detection_wine`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Outlier detection on a real data set</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This is an example showing the prediction latency of various scikit-learn estimators.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_prediction_latency_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_prediction_latency`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Prediction Latency</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Modeling species&#x27; geographic distributions is an important problem in conservation biology. In this example, we model the geographic distribution of two South American mammals given past observations and 14 environmental variables. Since we have only positive examples (there are no unsuccessful observations), we cast this problem as a density estimation problem and use the OneClassSVM as our modeling tool. The dataset is provided by Phillips et. al. (2006). If available, the example uses basemap to plot the coast lines and national boundaries of South America.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_species_distribution_modeling_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_species_distribution_modeling`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Species distribution modeling</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This notebook introduces different strategies to leverage time-related features for a bike sharing demand regression task that is highly dependent on business cycles (days, weeks, months) and yearly season cycles.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_cyclical_feature_engineering_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_cyclical_feature_engineering`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Time-related feature engineering</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This is an example of applying NMF and LatentDirichletAllocation on a corpus of documents and extract additive models of the topic structure of the corpus.  The output is a plot of topics, each represented as bar plot using top few words based on weights.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_topics_extraction_with_nmf_lda`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example employs several unsupervised learning techniques to extract the stock market structure from variations in historical quotes.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_stock_market_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_stock_market`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Visualizing the stock market structure</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A classical way to assert the relative importance of vertices in a graph is to compute the principal eigenvector of the adjacency matrix so as to assign to each vertex the values of the components of the first eigenvector as a centrality score: https://en.wikipedia.org/wiki/Eigenvector_centrality. On the graph of webpages and links those values are called the PageRank scores by Google.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_wikipedia_principal_eigenvector_thumb.png
    :alt:

  :doc:`/auto_examples/applications/wikipedia_principal_eigenvector`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Wikipedia principal eigenvector</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_tomography_l1_reconstruction
   /auto_examples/applications/plot_face_recognition
   /auto_examples/applications/plot_digits_denoising
   /auto_examples/applications/plot_time_series_lagged_features
   /auto_examples/applications/plot_model_complexity_influence
   /auto_examples/applications/plot_out_of_core_classification
   /auto_examples/applications/plot_outlier_detection_wine
   /auto_examples/applications/plot_prediction_latency
   /auto_examples/applications/plot_species_distribution_modeling
   /auto_examples/applications/plot_cyclical_feature_engineering
   /auto_examples/applications/plot_topics_extraction_with_nmf_lda
   /auto_examples/applications/plot_stock_market
   /auto_examples/applications/wikipedia_principal_eigenvector

