
.. 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-871adbdf-2650-48fa-b392-5322d0fbfbfe {color: black;background-color: white;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe pre{padding: 0;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-toggleable {background-color: white;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe 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-871adbdf-2650-48fa-b392-5322d0fbfbfe 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-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-estimator:hover {background-color: #d4ebff;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-item {z-index: 1;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-parallel-item:only-child::after {width: 0;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe 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-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-871adbdf-2650-48fa-b392-5322d0fbfbfe div.sk-container {display: inline-block;position: relative;}</style><div id="sk-871adbdf-2650-48fa-b392-5322d0fbfbfe" 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="b03d1d5a-4ed0-40f9-bbce-b6e433d6bd50" type="checkbox" ><label class="sk-toggleable__label" for="b03d1d5a-4ed0-40f9-bbce-b6e433d6bd50">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="77c2bae3-1e54-4dd0-9cce-4223a1b9933f" type="checkbox" ><label class="sk-toggleable__label" for="77c2bae3-1e54-4dd0-9cce-4223a1b9933f">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="bb06b45f-ffb2-4233-82d8-8507ea9b7e58" type="checkbox" ><label class="sk-toggleable__label" for="bb06b45f-ffb2-4233-82d8-8507ea9b7e58">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-076b467c-ced7-4cfd-86e4-c674c6773ebd {color: black;background-color: white;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd pre{padding: 0;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-toggleable {background-color: white;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd 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-076b467c-ced7-4cfd-86e4-c674c6773ebd 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-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-estimator:hover {background-color: #d4ebff;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-item {z-index: 1;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-parallel-item:only-child::after {width: 0;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd 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-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-076b467c-ced7-4cfd-86e4-c674c6773ebd div.sk-container {display: inline-block;position: relative;}</style><div id="sk-076b467c-ced7-4cfd-86e4-c674c6773ebd" 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="d4c0fe99-f98a-4f06-b32b-ecf530d815ce" type="checkbox" ><label class="sk-toggleable__label" for="d4c0fe99-f98a-4f06-b32b-ecf530d815ce">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="da0c9937-6716-4a30-a66a-4a9a0b420e43" type="checkbox" ><label class="sk-toggleable__label" for="da0c9937-6716-4a30-a66a-4a9a0b420e43">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="6ddb9cb7-8497-4d48-a484-42955e50c4a4" type="checkbox" ><label class="sk-toggleable__label" for="6ddb9cb7-8497-4d48-a484-42955e50c4a4">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="d9a85ae3-c9e1-4690-be05-347538ae9990" type="checkbox" ><label class="sk-toggleable__label" for="d9a85ae3-c9e1-4690-be05-347538ae9990">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-44189fb2-55fa-4f16-a45a-77ec83162c0c {color: black;background-color: white;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c pre{padding: 0;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-toggleable {background-color: white;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c 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-44189fb2-55fa-4f16-a45a-77ec83162c0c 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-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-estimator:hover {background-color: #d4ebff;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-item {z-index: 1;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-parallel-item:only-child::after {width: 0;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c 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-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-44189fb2-55fa-4f16-a45a-77ec83162c0c div.sk-container {display: inline-block;position: relative;}</style><div id="sk-44189fb2-55fa-4f16-a45a-77ec83162c0c" 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="37b3a1b1-fcc7-40f0-a112-00223c8c92dd" type="checkbox" ><label class="sk-toggleable__label" for="37b3a1b1-fcc7-40f0-a112-00223c8c92dd">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="2ffdda98-a753-478d-8643-26a4a408989c" type="checkbox" ><label class="sk-toggleable__label" for="2ffdda98-a753-478d-8643-26a4a408989c">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="fdc298f6-3570-4f6e-acda-29c246a0c64a" type="checkbox" ><label class="sk-toggleable__label" for="fdc298f6-3570-4f6e-acda-29c246a0c64a">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-264bf623-fca1-4fc5-aa1e-385cb94a08a7 {color: black;background-color: white;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 pre{padding: 0;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-toggleable {background-color: white;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 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-264bf623-fca1-4fc5-aa1e-385cb94a08a7 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-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-item {z-index: 1;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-parallel-item:only-child::after {width: 0;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 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-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7 div.sk-container {display: inline-block;position: relative;}</style><div id="sk-264bf623-fca1-4fc5-aa1e-385cb94a08a7" 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="a8ec908b-2f9f-405f-a644-424c9b5b4eb0" type="checkbox" ><label class="sk-toggleable__label" for="a8ec908b-2f9f-405f-a644-424c9b5b4eb0">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="00f38669-7dca-45e0-b713-4516e944b301" type="checkbox" ><label class="sk-toggleable__label" for="00f38669-7dca-45e0-b713-4516e944b301">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="ed6af40e-b3b3-4cd8-851f-591b92c69836" type="checkbox" ><label class="sk-toggleable__label" for="ed6af40e-b3b3-4cd8-851f-591b92c69836">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="74dff6bb-d76b-4f6e-84f2-235b0c29b16a" type="checkbox" ><label class="sk-toggleable__label" for="74dff6bb-d76b-4f6e-84f2-235b0c29b16a">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="3a502f30-67cb-4a60-9c42-b035ffe1c967" type="checkbox" ><label class="sk-toggleable__label" for="3a502f30-67cb-4a60-9c42-b035ffe1c967">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="f93697ca-5b83-4d44-a727-a08e487f202f" type="checkbox" ><label class="sk-toggleable__label" for="f93697ca-5b83-4d44-a727-a08e487f202f">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="a48e3d88-2725-43f9-beb1-0e70d1659110" type="checkbox" ><label class="sk-toggleable__label" for="a48e3d88-2725-43f9-beb1-0e70d1659110">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="57367146-623c-47a7-8670-a9634dee8027" type="checkbox" ><label class="sk-toggleable__label" for="57367146-623c-47a7-8670-a9634dee8027">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="4153295e-302a-47d8-8caf-93203409b026" type="checkbox" ><label class="sk-toggleable__label" for="4153295e-302a-47d8-8caf-93203409b026">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-cb0ba8cd-eb82-4131-8598-9f454421fbf1 {color: black;background-color: white;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 pre{padding: 0;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-toggleable {background-color: white;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 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-cb0ba8cd-eb82-4131-8598-9f454421fbf1 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-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-item {z-index: 1;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-parallel-item:only-child::after {width: 0;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 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-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1 div.sk-container {display: inline-block;position: relative;}</style><div id="sk-cb0ba8cd-eb82-4131-8598-9f454421fbf1" 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="1c2feabb-f913-42af-b6dc-59b11d3eba8e" type="checkbox" ><label class="sk-toggleable__label" for="1c2feabb-f913-42af-b6dc-59b11d3eba8e">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="e196085b-11c4-4103-b75b-6e2edca51116" type="checkbox" ><label class="sk-toggleable__label" for="e196085b-11c4-4103-b75b-6e2edca51116">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="606b0c14-8418-498a-b634-cf7472748f30" type="checkbox" ><label class="sk-toggleable__label" for="606b0c14-8418-498a-b634-cf7472748f30">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="73f59988-d72a-4fd8-8c62-5ac850c45b21" type="checkbox" ><label class="sk-toggleable__label" for="73f59988-d72a-4fd8-8c62-5ac850c45b21">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="87bea374-bfbd-4e46-b80a-1ab1370ea7ff" type="checkbox" ><label class="sk-toggleable__label" for="87bea374-bfbd-4e46-b80a-1ab1370ea7ff">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="55c66128-2479-4345-86a5-5f1c244c583b" type="checkbox" ><label class="sk-toggleable__label" for="55c66128-2479-4345-86a5-5f1c244c583b">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="52c413bf-9c8e-49a9-8b1e-a7b4a3e6b88a" type="checkbox" ><label class="sk-toggleable__label" for="52c413bf-9c8e-49a9-8b1e-a7b4a3e6b88a">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="a4649a55-8634-4ec7-ab40-e8bed678981a" type="checkbox" ><label class="sk-toggleable__label" for="a4649a55-8634-4ec7-ab40-e8bed678981a">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="2b07b26f-d44b-43e6-bcb0-e51a971694d1" type="checkbox" ><label class="sk-toggleable__label" for="2b07b26f-d44b-43e6-bcb0-e51a971694d1">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  0.071 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>`_
