
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/miscellaneous/plot_pipeline_display.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_miscellaneous_plot_pipeline_display.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py:


=================================================================
Displaying Pipelines
=================================================================

The default configuration for displaying a pipeline is `'text'` where
`set_config(display='text')`.  To visualize the diagram in Jupyter Notebook,
use `set_config(display='diagram')` and then output the pipeline object.

To see more detailed steps in the visualization of the pipeline, click on the
steps in the pipeline.

.. GENERATED FROM PYTHON SOURCE LINES 15-21

Displaying a Pipeline with a Preprocessing Step and Classifier
###############################################################################
 This section constructs a :class:`~sklearn.pipeline.Pipeline` with a preprocessing
 step, :class:`~sklearn.preprocessing.StandardScaler`, and classifier,
 :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 21-33

.. code-block:: default


    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LogisticRegression
    from sklearn import set_config

    steps = [
        ("preprocessing", StandardScaler()),
        ("classifier", LogisticRegression()),
    ]
    pipe = Pipeline(steps)








.. GENERATED FROM PYTHON SOURCE LINES 34-35

To view the text pipeline, the default is `display='text'`.

.. GENERATED FROM PYTHON SOURCE LINES 35-38

.. code-block:: default

    set_config(display="text")
    pipe





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    Pipeline(steps=[('preprocessing', StandardScaler()),
                    ('classifier', LogisticRegression())])



.. GENERATED FROM PYTHON SOURCE LINES 39-40

To visualize the diagram, change `display='diagram'`.

.. GENERATED FROM PYTHON SOURCE LINES 40-43

.. code-block:: default

    set_config(display="diagram")
    pipe  # click on the diagram below to see the details of each step






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-0b63b988-f3b8-479a-991c-a1184daa1604 {color: black;background-color: white;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 pre{padding: 0;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-toggleable {background-color: white;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-estimator:hover {background-color: #d4ebff;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-item {z-index: 1;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-parallel-item:only-child::after {width: 0;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-0b63b988-f3b8-479a-991c-a1184daa1604 div.sk-container {display: inline-block;position: relative;}</style><div id="sk-0b63b988-f3b8-479a-991c-a1184daa1604" class"sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="61f2dcf6-7722-44a9-bcdf-849a967897b6" type="checkbox" ><label class="sk-toggleable__label" for="61f2dcf6-7722-44a9-bcdf-849a967897b6">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('preprocessing', StandardScaler()),
                    ('classifier', LogisticRegression())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="645082c5-c2fc-4eb0-80b4-14589f8105fd" type="checkbox" ><label class="sk-toggleable__label" for="645082c5-c2fc-4eb0-80b4-14589f8105fd">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3549ed83-6bdc-4d7a-86de-7c5efb0ebd9a" type="checkbox" ><label class="sk-toggleable__label" for="3549ed83-6bdc-4d7a-86de-7c5efb0ebd9a">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 44-51

Displaying a Pipeline Chaining Multiple Preprocessing Steps & Classifier
###############################################################################
 This section constructs a :class:`~sklearn.pipeline.Pipeline` with multiple
 preprocessing steps, :class:`~sklearn.preprocessing.PolynomialFeatures` and
 :class:`~sklearn.preprocessing.StandardScaler`, and a classifer step,
 :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 51-64

.. code-block:: default


    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler, PolynomialFeatures
    from sklearn.linear_model import LogisticRegression
    from sklearn import set_config

    steps = [
        ("standard_scaler", StandardScaler()),
        ("polynomial", PolynomialFeatures(degree=3)),
        ("classifier", LogisticRegression(C=2.0)),
    ]
    pipe = Pipeline(steps)








.. GENERATED FROM PYTHON SOURCE LINES 65-66

To visualize the diagram, change to display='diagram'

.. GENERATED FROM PYTHON SOURCE LINES 66-69

.. code-block:: default

    set_config(display="diagram")
    pipe  # click on the diagram below to see the details of each step






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 {color: black;background-color: white;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 pre{padding: 0;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-toggleable {background-color: white;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-estimator:hover {background-color: #d4ebff;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-item {z-index: 1;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-parallel-item:only-child::after {width: 0;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540 div.sk-container {display: inline-block;position: relative;}</style><div id="sk-cf0a7df8-7e0a-48b9-b3c7-37a1d0f9f540" class"sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b57b297e-96ad-44af-862e-aeeb8f6af3a1" type="checkbox" ><label class="sk-toggleable__label" for="b57b297e-96ad-44af-862e-aeeb8f6af3a1">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('standard_scaler', StandardScaler()),
                    ('polynomial', PolynomialFeatures(degree=3)),
                    ('classifier', LogisticRegression(C=2.0))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a5d713c3-74bd-4ea5-b23b-0febbceb488e" type="checkbox" ><label class="sk-toggleable__label" for="a5d713c3-74bd-4ea5-b23b-0febbceb488e">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="26050902-ef8c-4e9f-ae74-9a44cbb6bd3e" type="checkbox" ><label class="sk-toggleable__label" for="26050902-ef8c-4e9f-ae74-9a44cbb6bd3e">PolynomialFeatures</label><div class="sk-toggleable__content"><pre>PolynomialFeatures(degree=3)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7bbc7b02-16a4-45be-abfe-4f3ce4a26939" type="checkbox" ><label class="sk-toggleable__label" for="7bbc7b02-16a4-45be-abfe-4f3ce4a26939">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=2.0)</pre></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 70-76

Displaying a Pipeline and Dimensionality Reduction and Classifier
###############################################################################
 This section constructs a :class:`~sklearn.pipeline.Pipeline` with a
 dimensionality reduction step, :class:`~sklearn.decomposition.PCA`,
 a classifier, :class:`~sklearn.svm.SVC`, and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 76-85

.. code-block:: default


    from sklearn.pipeline import Pipeline
    from sklearn.svm import SVC
    from sklearn.decomposition import PCA
    from sklearn import set_config

    steps = [("reduce_dim", PCA(n_components=4)), ("classifier", SVC(kernel="linear"))]
    pipe = Pipeline(steps)








.. GENERATED FROM PYTHON SOURCE LINES 86-87

To visualize the diagram, change to `display='diagram'`.

.. GENERATED FROM PYTHON SOURCE LINES 87-90

.. code-block:: default

    set_config(display="diagram")
    pipe  # click on the diagram below to see the details of each step






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f {color: black;background-color: white;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f pre{padding: 0;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-toggleable {background-color: white;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-estimator:hover {background-color: #d4ebff;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-item {z-index: 1;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-parallel-item:only-child::after {width: 0;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-74c2b652-89c8-4ee9-80d7-8876145fb30f div.sk-container {display: inline-block;position: relative;}</style><div id="sk-74c2b652-89c8-4ee9-80d7-8876145fb30f" class"sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="69a42e67-784a-4421-9cf6-dd7ba20ed9c0" type="checkbox" ><label class="sk-toggleable__label" for="69a42e67-784a-4421-9cf6-dd7ba20ed9c0">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('reduce_dim', PCA(n_components=4)),
                    ('classifier', SVC(kernel='linear'))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="5e87cfd1-d0d7-4422-a63e-3e112e0ea61e" type="checkbox" ><label class="sk-toggleable__label" for="5e87cfd1-d0d7-4422-a63e-3e112e0ea61e">PCA</label><div class="sk-toggleable__content"><pre>PCA(n_components=4)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="84e40f68-51dd-472d-93df-9dfe656bb817" type="checkbox" ><label class="sk-toggleable__label" for="84e40f68-51dd-472d-93df-9dfe656bb817">SVC</label><div class="sk-toggleable__content"><pre>SVC(kernel='linear')</pre></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 91-97

Displaying a Complex Pipeline Chaining a Column Transformer
###############################################################################
 This section constructs a complex :class:`~sklearn.pipeline.Pipeline` with a
 :class:`~sklearn.compose.ColumnTransformer` and a classifier,
 :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 97-133

.. code-block:: default


    import numpy as np
    from sklearn.pipeline import make_pipeline
    from sklearn.pipeline import Pipeline
    from sklearn.impute import SimpleImputer
    from sklearn.compose import ColumnTransformer
    from sklearn.preprocessing import OneHotEncoder, StandardScaler
    from sklearn.linear_model import LogisticRegression
    from sklearn import set_config

    numeric_preprocessor = Pipeline(
        steps=[
            ("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")),
            ("scaler", StandardScaler()),
        ]
    )

    categorical_preprocessor = Pipeline(
        steps=[
            (
                "imputation_constant",
                SimpleImputer(fill_value="missing", strategy="constant"),
            ),
            ("onehot", OneHotEncoder(handle_unknown="ignore")),
        ]
    )

    preprocessor = ColumnTransformer(
        [
            ("categorical", categorical_preprocessor, ["state", "gender"]),
            ("numerical", numeric_preprocessor, ["age", "weight"]),
        ]
    )

    pipe = make_pipeline(preprocessor, LogisticRegression(max_iter=500))








.. GENERATED FROM PYTHON SOURCE LINES 134-135

To visualize the diagram, change to `display='diagram'`

.. GENERATED FROM PYTHON SOURCE LINES 135-138

.. code-block:: default

    set_config(display="diagram")
    pipe  # click on the diagram below to see the details of each step






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f {color: black;background-color: white;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f pre{padding: 0;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-toggleable {background-color: white;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-estimator:hover {background-color: #d4ebff;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-item {z-index: 1;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-parallel-item:only-child::after {width: 0;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f div.sk-container {display: inline-block;position: relative;}</style><div id="sk-c64933e0-33d0-4a5c-89af-7cd8e6eab48f" class"sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="116711f1-5b8a-4423-a572-eee7a21d082f" type="checkbox" ><label class="sk-toggleable__label" for="116711f1-5b8a-4423-a572-eee7a21d082f">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('categorical',
                                                      Pipeline(steps=[('imputation_constant',
                                                                       SimpleImputer(fill_value='missing',
                                                                                     strategy='constant')),
                                                                      ('onehot',
                                                                       OneHotEncoder(handle_unknown='ignore'))]),
                                                      ['state', 'gender']),
                                                     ('numerical',
                                                      Pipeline(steps=[('imputation_mean',
                                                                       SimpleImputer()),
                                                                      ('scaler',
                                                                       StandardScaler())]),
                                                      ['age', 'weight'])])),
                    ('logisticregression', LogisticRegression(max_iter=500))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="0581b5fb-2fa0-4056-95d7-8a533ad55a6d" type="checkbox" ><label class="sk-toggleable__label" for="0581b5fb-2fa0-4056-95d7-8a533ad55a6d">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('categorical',
                                     Pipeline(steps=[('imputation_constant',
                                                      SimpleImputer(fill_value='missing',
                                                                    strategy='constant')),
                                                     ('onehot',
                                                      OneHotEncoder(handle_unknown='ignore'))]),
                                     ['state', 'gender']),
                                    ('numerical',
                                     Pipeline(steps=[('imputation_mean',
                                                      SimpleImputer()),
                                                     ('scaler', StandardScaler())]),
                                     ['age', 'weight'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="927ccd35-d6e9-4ba5-9993-bc37708b0754" type="checkbox" ><label class="sk-toggleable__label" for="927ccd35-d6e9-4ba5-9993-bc37708b0754">categorical</label><div class="sk-toggleable__content"><pre>['state', 'gender']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7d66c020-6a62-4e4d-a5f0-78100af96a4c" type="checkbox" ><label class="sk-toggleable__label" for="7d66c020-6a62-4e4d-a5f0-78100af96a4c">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(fill_value='missing', strategy='constant')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e8630d00-1ac5-4fc7-91c6-f1e632a727f6" type="checkbox" ><label class="sk-toggleable__label" for="e8630d00-1ac5-4fc7-91c6-f1e632a727f6">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c9b0639f-a602-4f5b-830d-9ad430acd7d1" type="checkbox" ><label class="sk-toggleable__label" for="c9b0639f-a602-4f5b-830d-9ad430acd7d1">numerical</label><div class="sk-toggleable__content"><pre>['age', 'weight']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="84653a81-2355-47b1-8d39-0272740f77d2" type="checkbox" ><label class="sk-toggleable__label" for="84653a81-2355-47b1-8d39-0272740f77d2">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6d844361-15bf-4c43-8fba-fb81e142c842" type="checkbox" ><label class="sk-toggleable__label" for="6d844361-15bf-4c43-8fba-fb81e142c842">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="da557223-c3ae-4c5c-9603-1db7ef880c7a" type="checkbox" ><label class="sk-toggleable__label" for="da557223-c3ae-4c5c-9603-1db7ef880c7a">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(max_iter=500)</pre></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 139-145

Displaying a Grid Search over a Pipeline with a Classifier
###############################################################################
 This section constructs a :class:`~sklearn.model_selection.GridSearchCV`
 over a :class:`~sklearn.pipeline.Pipeline` with
 :class:`~sklearn.ensemble.RandomForestClassifier` and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 145-193

.. code-block:: default


    import numpy as np
    from sklearn.pipeline import make_pipeline
    from sklearn.pipeline import Pipeline
    from sklearn.impute import SimpleImputer
    from sklearn.compose import ColumnTransformer
    from sklearn.preprocessing import OneHotEncoder, StandardScaler
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import GridSearchCV
    from sklearn import set_config

    numeric_preprocessor = Pipeline(
        steps=[
            ("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")),
            ("scaler", StandardScaler()),
        ]
    )

    categorical_preprocessor = Pipeline(
        steps=[
            (
                "imputation_constant",
                SimpleImputer(fill_value="missing", strategy="constant"),
            ),
            ("onehot", OneHotEncoder(handle_unknown="ignore")),
        ]
    )

    preprocessor = ColumnTransformer(
        [
            ("categorical", categorical_preprocessor, ["state", "gender"]),
            ("numerical", numeric_preprocessor, ["age", "weight"]),
        ]
    )

    pipe = Pipeline(
        steps=[("preprocessor", preprocessor), ("classifier", RandomForestClassifier())]
    )

    param_grid = {
        "classifier__n_estimators": [200, 500],
        "classifier__max_features": ["auto", "sqrt", "log2"],
        "classifier__max_depth": [4, 5, 6, 7, 8],
        "classifier__criterion": ["gini", "entropy"],
    }

    grid_search = GridSearchCV(pipe, param_grid=param_grid, n_jobs=1)








.. GENERATED FROM PYTHON SOURCE LINES 194-195

To visualize the diagram, change to `display='diagram'`.

.. GENERATED FROM PYTHON SOURCE LINES 195-197

.. code-block:: default

    set_config(display="diagram")
    grid_search  # click on the diagram below to see the details of each step





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 {color: black;background-color: white;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 pre{padding: 0;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-toggleable {background-color: white;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-estimator:hover {background-color: #d4ebff;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-item {z-index: 1;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-parallel-item:only-child::after {width: 0;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-dd9760c0-9c7f-4a15-8bcf-90a287830884 div.sk-container {display: inline-block;position: relative;}</style><div id="sk-dd9760c0-9c7f-4a15-8bcf-90a287830884" class"sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="2643c012-1cbe-44b1-9514-4eee828bb0f4" type="checkbox" ><label class="sk-toggleable__label" for="2643c012-1cbe-44b1-9514-4eee828bb0f4">GridSearchCV</label><div class="sk-toggleable__content"><pre>GridSearchCV(estimator=Pipeline(steps=[('preprocessor',
                                            ColumnTransformer(transformers=[('categorical',
                                                                             Pipeline(steps=[('imputation_constant',
                                                                                              SimpleImputer(fill_value='missing',
                                                                                                            strategy='constant')),
                                                                                             ('onehot',
                                                                                              OneHotEncoder(handle_unknown='ignore'))]),
                                                                             ['state',
                                                                              'gender']),
                                                                            ('numerical',
                                                                             Pipeline(steps=[('imputation_mean',
                                                                                              SimpleImputer()),
                                                                                             ('scaler',
                                                                                              StandardScaler())]),
                                                                             ['age',
                                                                              'weight'])])),
                                           ('classifier',
                                            RandomForestClassifier())]),
                 n_jobs=1,
                 param_grid={'classifier__criterion': ['gini', 'entropy'],
                             'classifier__max_depth': [4, 5, 6, 7, 8],
                             'classifier__max_features': ['auto', 'sqrt', 'log2'],
                             'classifier__n_estimators': [200, 500]})</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b96fdf90-7414-4e2c-9c9e-e60923593b21" type="checkbox" ><label class="sk-toggleable__label" for="b96fdf90-7414-4e2c-9c9e-e60923593b21">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('categorical',
                                     Pipeline(steps=[('imputation_constant',
                                                      SimpleImputer(fill_value='missing',
                                                                    strategy='constant')),
                                                     ('onehot',
                                                      OneHotEncoder(handle_unknown='ignore'))]),
                                     ['state', 'gender']),
                                    ('numerical',
                                     Pipeline(steps=[('imputation_mean',
                                                      SimpleImputer()),
                                                     ('scaler', StandardScaler())]),
                                     ['age', 'weight'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="45064b1e-0493-4d7b-8e0c-4a405799476c" type="checkbox" ><label class="sk-toggleable__label" for="45064b1e-0493-4d7b-8e0c-4a405799476c">categorical</label><div class="sk-toggleable__content"><pre>['state', 'gender']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3c9b8112-8b06-4348-aee8-e24eed0acf27" type="checkbox" ><label class="sk-toggleable__label" for="3c9b8112-8b06-4348-aee8-e24eed0acf27">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(fill_value='missing', strategy='constant')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b52dc7bb-c07c-4fb3-923f-9f25bbc9b1de" type="checkbox" ><label class="sk-toggleable__label" for="b52dc7bb-c07c-4fb3-923f-9f25bbc9b1de">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="068d3e29-21c0-4af0-ab74-fd8ea02d37c4" type="checkbox" ><label class="sk-toggleable__label" for="068d3e29-21c0-4af0-ab74-fd8ea02d37c4">numerical</label><div class="sk-toggleable__content"><pre>['age', 'weight']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b8b1129b-1885-46a2-82c5-5fca277917e4" type="checkbox" ><label class="sk-toggleable__label" for="b8b1129b-1885-46a2-82c5-5fca277917e4">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="bdd208e6-4f15-4d7d-b37e-39dfe00a2391" type="checkbox" ><label class="sk-toggleable__label" for="bdd208e6-4f15-4d7d-b37e-39dfe00a2391">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="5f64b675-9ccb-4cdb-926b-a849ca85b313" type="checkbox" ><label class="sk-toggleable__label" for="5f64b675-9ccb-4cdb-926b-a849ca85b313">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.089 seconds)


.. _sphx_glr_download_auto_examples_miscellaneous_plot_pipeline_display.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_pipeline_display.py <plot_pipeline_display.py>`



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

     :download:`Download Jupyter notebook: plot_pipeline_display.ipynb <plot_pipeline_display.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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