.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/miscellaneous/plot_estimator_representation.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_miscellaneous_plot_estimator_representation.py: =========================================== Displaying estimators and complex pipelines =========================================== This example illustrates different ways estimators and pipelines can be displayed. .. GENERATED FROM PYTHON SOURCE LINES 9-19 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause from sklearn.compose import make_column_transformer from sklearn.impute import SimpleImputer from sklearn.linear_model import LogisticRegression from sklearn.pipeline import make_pipeline from sklearn.preprocessing import OneHotEncoder, StandardScaler .. GENERATED FROM PYTHON SOURCE LINES 20-26 Compact text representation --------------------------- Estimators will only show the parameters that have been set to non-default values when displayed as a string. This reduces the visual noise and makes it easier to spot what the differences are when comparing instances. .. GENERATED FROM PYTHON SOURCE LINES 26-30 .. code-block:: Python lr = LogisticRegression(penalty="l1") print(lr) .. rst-class:: sphx-glr-script-out .. code-block:: none LogisticRegression(penalty='l1') .. GENERATED FROM PYTHON SOURCE LINES 31-39 Rich HTML representation ------------------------ In notebooks estimators and pipelines will use a rich HTML representation. This is particularly useful to summarise the structure of pipelines and other composite estimators, with interactivity to provide detail. Click on the example image below to expand Pipeline elements. See :ref:`visualizing_composite_estimators` for how you can use this feature. .. GENERATED FROM PYTHON SOURCE LINES 39-53 .. code-block:: Python num_proc = make_pipeline(SimpleImputer(strategy="median"), StandardScaler()) cat_proc = make_pipeline( SimpleImputer(strategy="constant", fill_value="missing"), OneHotEncoder(handle_unknown="ignore"), ) preprocessor = make_column_transformer( (num_proc, ("feat1", "feat3")), (cat_proc, ("feat0", "feat2")) ) clf = make_pipeline(preprocessor, LogisticRegression()) clf .. raw:: html 
Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('pipeline-1',
                                                      Pipeline(steps=[('simpleimputer',
                                                                       SimpleImputer(strategy='median')),
                                                                      ('standardscaler',
                                                                       StandardScaler())]),
                                                      ('feat1', 'feat3')),
                                                     ('pipeline-2',
                                                      Pipeline(steps=[('simpleimputer',
                                                                       SimpleImputer(fill_value='missing',
                                                                                     strategy='constant')),
                                                                      ('onehotencoder',
                                                                       OneHotEncoder(handle_unknown='ignore'))]),
                                                      ('feat0', 'feat2'))])),
                    ('logisticregression', LogisticRegression())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.027 seconds) .. _sphx_glr_download_auto_examples_miscellaneous_plot_estimator_representation.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_estimator_representation.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/miscellaneous/plot_estimator_representation.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_estimator_representation.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_estimator_representation.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_estimator_representation.zip ` .. include:: plot_estimator_representation.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_