.. 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 Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_miscellaneous_plot_estimator_representation.py: =========================================== 显示估计器和复杂的管道 =========================================== 这个例子展示了显示估计器和管道的不同方法。 .. GENERATED FROM PYTHON SOURCE LINES 8-15 .. code-block:: Python 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 16-20 紧凑文本表示 --------------------------- 估计器在作为字符串显示时，只会显示已设置为非默认值的参数。这减少了视觉噪音，并使比较实例时更容易发现差异。 .. GENERATED FROM PYTHON SOURCE LINES 20-24 .. 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 25-28 丰富的HTML表示 ---------------- 在笔记本中，估计器和管道将使用丰富的HTML表示。这对于总结管道和其他复合估计器的结构特别有用，并具有交互性以提供详细信息。点击下面的示例图像以展开管道元素。有关如何使用此功能，请参见 :ref:`visualizing_composite_estimators` 。 .. GENERATED FROM PYTHON SOURCE LINES 28-43 .. 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 0.012 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/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_estimator_representation.ipynb :alt: Launch binder :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 `_