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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_kds_plot_report_script.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_kds_plot_report_script.py:


plot_report with examples
==================================

An example showing the :py:func:`~scikitplot.kds.report` function used
by a scikit-learn classifier.

.. GENERATED FROM PYTHON SOURCE LINES 8-12

.. code-block:: Python
   :lineno-start: 9


    # Authors: The scikit-plots developers
    # SPDX-License-Identifier: BSD-3-Clause








.. GENERATED FROM PYTHON SOURCE LINES 13-38

.. code-block:: Python
   :lineno-start: 13

    from sklearn.datasets import (
        load_breast_cancer as data_2_classes,
        # load_iris as data_3_classes,
    )
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split

    # importing pylab or pyplot
    import matplotlib.pyplot as plt
    import numpy as np; np.random.seed(0)  # reproducibility

    # Import scikit-plot
    import scikitplot as sp

    # Load the data
    X, y = data_2_classes(return_X_y=True, as_frame=False)
    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)

    # Create an instance of the LogisticRegression
    model = LogisticRegression(max_iter=int(1e5), random_state=0).fit(X_train, y_train)

    # Perform predictions
    y_val_prob = model.predict_proba(X_val)









.. GENERATED FROM PYTHON SOURCE LINES 39-51

.. code-block:: Python
   :lineno-start: 39

    sp.kds.decile_table(
        y_val,
        y_val_prob,
        save_fig=True,
        save_fig_filename="",
        # overwrite=True,
        add_timestamp=True,
        # verbose=True,
        display_term_tables=True,
    )






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

 .. code-block:: none

    LABELS INFO:

     prob_min         : Minimum probability in a particular decile
     prob_max         : Minimum probability in a particular decile
     prob_avg         : Average probability in a particular decile
     cnt_events       : Count of events in a particular decile
     cnt_resp         : Count of responders in a particular decile
     cnt_non_resp     : Count of non-responders in a particular decile
     cnt_resp_rndm    : Count of responders if events assigned randomly in a particular decile
     cnt_resp_wiz     : Count of best possible responders in a particular decile
     resp_rate        : Response Rate in a particular decile [(cnt_resp/cnt_cust)*100]
     cum_events       : Cumulative sum of events decile-wise 
     cum_resp         : Cumulative sum of responders decile-wise 
     cum_resp_wiz     : Cumulative sum of best possible responders decile-wise 
     cum_non_resp     : Cumulative sum of non-responders decile-wise 
     cum_events_pct   : Cumulative sum of percentages of events decile-wise 
     cum_resp_pct     : Cumulative sum of percentages of responders decile-wise 
     cum_resp_pct_wiz : Cumulative sum of percentages of best possible responders decile-wise 
     cum_non_resp_pct : Cumulative sum of percentages of non-responders decile-wise 
     KS               : KS Statistic decile-wise 
     lift             : Cumuative Lift Value decile-wise


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>decile</th>
          <th>prob_min</th>
          <th>prob_max</th>
          <th>prob_avg</th>
          <th>cnt_cust</th>
          <th>cnt_resp</th>
          <th>cnt_non_resp</th>
          <th>cnt_resp_rndm</th>
          <th>cnt_resp_wiz</th>
          <th>resp_rate</th>
          <th>cum_cust</th>
          <th>cum_resp</th>
          <th>cum_resp_wiz</th>
          <th>cum_non_resp</th>
          <th>cum_cust_pct</th>
          <th>cum_resp_pct</th>
          <th>cum_resp_pct_wiz</th>
          <th>cum_non_resp_pct</th>
          <th>KS</th>
          <th>lift</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>1.000</td>
          <td>1.000</td>
          <td>1.000</td>
          <td>29.0</td>
          <td>29.0</td>
          <td>0.0</td>
          <td>18.4</td>
          <td>29</td>
          <td>100.000</td>
          <td>29.0</td>
          <td>29.0</td>
          <td>29</td>
          <td>0.0</td>
          <td>10.175</td>
          <td>15.761</td>
          <td>15.761</td>
          <td>0.000</td>
          <td>15.761</td>
          <td>1.549</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
          <td>0.999</td>
          <td>1.000</td>
          <td>1.000</td>
          <td>28.0</td>
          <td>28.0</td>
          <td>0.0</td>
          <td>18.4</td>
          <td>28</td>
          <td>100.000</td>
          <td>57.0</td>
          <td>57.0</td>
          <td>57</td>
          <td>0.0</td>
          <td>20.000</td>
          <td>30.978</td>
          <td>30.978</td>
          <td>0.000</td>
          <td>30.978</td>
          <td>1.549</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3</td>
          <td>0.998</td>
          <td>0.999</td>
          <td>0.999</td>
          <td>29.0</td>
          <td>29.0</td>
          <td>0.0</td>
          <td>18.4</td>
          <td>29</td>
          <td>100.000</td>
          <td>86.0</td>
          <td>86.0</td>
          <td>86</td>
          <td>0.0</td>
          <td>30.175</td>
          <td>46.739</td>
          <td>46.739</td>
          <td>0.000</td>
          <td>46.739</td>
          <td>1.549</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4</td>
          <td>0.993</td>
          <td>0.997</td>
          <td>0.996</td>
          <td>28.0</td>
          <td>28.0</td>
          <td>0.0</td>
          <td>18.4</td>
          <td>28</td>
          <td>100.000</td>
          <td>114.0</td>
          <td>114.0</td>
          <td>114</td>
          <td>0.0</td>
          <td>40.000</td>
          <td>61.957</td>
          <td>61.957</td>
          <td>0.000</td>
          <td>61.957</td>
          <td>1.549</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5</td>
          <td>0.960</td>
          <td>0.992</td>
          <td>0.980</td>
          <td>29.0</td>
          <td>29.0</td>
          <td>0.0</td>
          <td>18.4</td>
          <td>29</td>
          <td>100.000</td>
          <td>143.0</td>
          <td>143.0</td>
          <td>143</td>
          <td>0.0</td>
          <td>50.175</td>
          <td>77.717</td>
          <td>77.717</td>
          <td>0.000</td>
          <td>77.717</td>
          <td>1.549</td>
        </tr>
        <tr>
          <th>5</th>
          <td>6</td>
          <td>0.772</td>
          <td>0.956</td>
          <td>0.888</td>
          <td>28.0</td>
          <td>25.0</td>
          <td>3.0</td>
          <td>18.4</td>
          <td>28</td>
          <td>89.286</td>
          <td>171.0</td>
          <td>168.0</td>
          <td>171</td>
          <td>3.0</td>
          <td>60.000</td>
          <td>91.304</td>
          <td>92.935</td>
          <td>2.970</td>
          <td>88.334</td>
          <td>1.522</td>
        </tr>
        <tr>
          <th>6</th>
          <td>7</td>
          <td>0.066</td>
          <td>0.769</td>
          <td>0.346</td>
          <td>29.0</td>
          <td>16.0</td>
          <td>13.0</td>
          <td>18.4</td>
          <td>13</td>
          <td>55.172</td>
          <td>200.0</td>
          <td>184.0</td>
          <td>184</td>
          <td>16.0</td>
          <td>70.175</td>
          <td>100.000</td>
          <td>100.000</td>
          <td>15.842</td>
          <td>84.158</td>
          <td>1.425</td>
        </tr>
        <tr>
          <th>7</th>
          <td>8</td>
          <td>0.000</td>
          <td>0.048</td>
          <td>0.011</td>
          <td>28.0</td>
          <td>0.0</td>
          <td>28.0</td>
          <td>18.4</td>
          <td>0</td>
          <td>0.000</td>
          <td>228.0</td>
          <td>184.0</td>
          <td>184</td>
          <td>44.0</td>
          <td>80.000</td>
          <td>100.000</td>
          <td>100.000</td>
          <td>43.564</td>
          <td>56.436</td>
          <td>1.250</td>
        </tr>
        <tr>
          <th>8</th>
          <td>9</td>
          <td>0.000</td>
          <td>0.000</td>
          <td>0.000</td>
          <td>29.0</td>
          <td>0.0</td>
          <td>29.0</td>
          <td>18.4</td>
          <td>0</td>
          <td>0.000</td>
          <td>257.0</td>
          <td>184.0</td>
          <td>184</td>
          <td>73.0</td>
          <td>90.175</td>
          <td>100.000</td>
          <td>100.000</td>
          <td>72.277</td>
          <td>27.723</td>
          <td>1.109</td>
        </tr>
        <tr>
          <th>9</th>
          <td>10</td>
          <td>0.000</td>
          <td>0.000</td>
          <td>0.000</td>
          <td>28.0</td>
          <td>0.0</td>
          <td>28.0</td>
          <td>18.4</td>
          <td>0</td>
          <td>0.000</td>
          <td>285.0</td>
          <td>184.0</td>
          <td>184</td>
          <td>101.0</td>
          <td>100.000</td>
          <td>100.000</td>
          <td>100.000</td>
          <td>100.000</td>
          <td>0.000</td>
          <td>1.000</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 52-53

Plot!

.. GENERATED FROM PYTHON SOURCE LINES 53-64

.. code-block:: Python
   :lineno-start: 53

    ax = sp.kds.report(
        y_val,
        y_val_prob,
        save_fig=True,
        save_fig_filename="",
        # overwrite=True,
        add_timestamp=True,
        # verbose=True,
        display_term_tables=True,
    )




.. image-sg:: /auto_examples/kds/images/sphx_glr_plot_report_script_001.png
   :alt: KS Statistic Plot
   :srcset: /auto_examples/kds/images/sphx_glr_plot_report_script_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    LABELS INFO:

     prob_min         : Minimum probability in a particular decile
     prob_max         : Minimum probability in a particular decile
     prob_avg         : Average probability in a particular decile
     cnt_events       : Count of events in a particular decile
     cnt_resp         : Count of responders in a particular decile
     cnt_non_resp     : Count of non-responders in a particular decile
     cnt_resp_rndm    : Count of responders if events assigned randomly in a particular decile
     cnt_resp_wiz     : Count of best possible responders in a particular decile
     resp_rate        : Response Rate in a particular decile [(cnt_resp/cnt_cust)*100]
     cum_events       : Cumulative sum of events decile-wise 
     cum_resp         : Cumulative sum of responders decile-wise 
     cum_resp_wiz     : Cumulative sum of best possible responders decile-wise 
     cum_non_resp     : Cumulative sum of non-responders decile-wise 
     cum_events_pct   : Cumulative sum of percentages of events decile-wise 
     cum_resp_pct     : Cumulative sum of percentages of responders decile-wise 
     cum_resp_pct_wiz : Cumulative sum of percentages of best possible responders decile-wise 
     cum_non_resp_pct : Cumulative sum of percentages of non-responders decile-wise 
     KS               : KS Statistic decile-wise 
     lift             : Cumuative Lift Value decile-wise




.. GENERATED FROM PYTHON SOURCE LINES 65-73

.. tags::

   model-type: classification
   model-workflow: model evaluation
   plot-type: line
   plot-type: cum-gain curve
   level: beginner
   purpose: showcase


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

   **Total running time of the script:** (0 minutes 1.265 seconds)


.. _sphx_glr_download_auto_examples_kds_plot_report_script.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-plots/scikit-plots/maintenance/0.4.X?urlpath=lab/tree/notebooks/auto_examples/kds/plot_report_script.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/kds/plot_report_script.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_report_script.zip <plot_report_script.zip>`


.. include:: plot_report_script.recommendations


.. only:: html

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

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