.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\3_models\plot_4_xgb1_reg.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_3_models_plot_4_xgb1_reg.py>`
to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_3_models_plot_4_xgb1_reg.py:
XGB-1 Regression (California Housing)
=====================================
.. GENERATED FROM PYTHON SOURCE LINES 8-9
Experiment initialization and data preparation
.. GENERATED FROM PYTHON SOURCE LINES 9-16
.. code-block:: Python
from piml import Experiment
from piml.models import XGB1Regressor
exp = Experiment()
exp.data_loader(data="CaliforniaHousing_trim2", silent=True)
exp.data_prepare(target="MedHouseVal", task_type="regression", silent=True)
.. GENERATED FROM PYTHON SOURCE LINES 17-18
Train model
.. GENERATED FROM PYTHON SOURCE LINES 18-20
.. code-block:: Python
exp.model_train(model=XGB1Regressor(n_estimators=500, max_bin=20, min_bin_size=0.01), name="XGB1")
.. GENERATED FROM PYTHON SOURCE LINES 21-22
Train model with monotonicity constrained on MedInc
.. GENERATED FROM PYTHON SOURCE LINES 22-26
.. code-block:: Python
exp.model_train(model=XGB1Regressor(n_estimators=500, max_bin=20, min_bin_size=0.01,
mono_increasing_list=("MedInc", )),
name="Mono-XGB1")
.. GENERATED FROM PYTHON SOURCE LINES 27-28
Evaluate predictive performance of XGB1
.. GENERATED FROM PYTHON SOURCE LINES 28-30
.. code-block:: Python
exp.model_diagnose(model="XGB1", show='accuracy_table')
.. rst-class:: sphx-glr-script-out
.. code-block:: none
MSE MAE R2
Train 0.0131 0.0816 0.7704
Test 0.0136 0.0822 0.7546
Gap 0.0005 0.0006 -0.0159
.. GENERATED FROM PYTHON SOURCE LINES 31-32
Evaluate predictive performance of Mono-XGB1
.. GENERATED FROM PYTHON SOURCE LINES 32-34
.. code-block:: Python
exp.model_diagnose(model="Mono-XGB1", show='accuracy_table')
.. rst-class:: sphx-glr-script-out
.. code-block:: none
MSE MAE R2
Train 0.0134 0.0831 0.7649
Test 0.0139 0.0837 0.7490
Gap 0.0005 0.0006 -0.0159
.. GENERATED FROM PYTHON SOURCE LINES 35-36
Global effect plot of XGB1 on MedInc
.. GENERATED FROM PYTHON SOURCE LINES 36-39
.. code-block:: Python
exp.model_interpret(model="XGB1", show="global_effect_plot", uni_feature="MedInc",
original_scale=True, figsize=(5, 4))
.. image-sg:: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_001.png
:alt: MedInc (15.2%)
:srcset: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_001.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 40-41
Global effect plot of Mono-XGB1 on MedInc
.. GENERATED FROM PYTHON SOURCE LINES 41-44
.. code-block:: Python
exp.model_interpret(model="Mono-XGB1", show="global_effect_plot", uni_feature="MedInc",
original_scale=True, figsize=(5, 4))
.. image-sg:: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_002.png
:alt: MedInc (17.6%)
:srcset: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_002.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 45-46
Feature importance of Mono-XGB1
.. GENERATED FROM PYTHON SOURCE LINES 46-48
.. code-block:: Python
exp.model_interpret(model="Mono-XGB1", show="global_fi", figsize=(5, 4))
.. image-sg:: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_003.png
:alt: Feature Importance
:srcset: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_003.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 49-50
Weight of evidence plot of Mono-XGB1 on MedInc
.. GENERATED FROM PYTHON SOURCE LINES 50-52
.. code-block:: Python
exp.model_interpret(model="Mono-XGB1", show="xgb1_woe", uni_feature="MedInc", original_scale=True, figsize=(5, 4))
.. image-sg:: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_004.png
:alt: WoE of MedInc (IV: 0.1253)
:srcset: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_004.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 53-54
Information value plot of Mono-XGB1
.. GENERATED FROM PYTHON SOURCE LINES 54-56
.. code-block:: Python
exp.model_interpret(model="Mono-XGB1", show="xgb1_iv", figsize=(5, 4))
.. image-sg:: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_005.png
:alt: Information Value
:srcset: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_005.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 57-58
Local interpretation of Mono-XGB1
.. GENERATED FROM PYTHON SOURCE LINES 58-59
.. code-block:: Python
exp.model_interpret(model="Mono-XGB1", show="local_fi", sample_id=0, original_scale=True, figsize=(5, 4))
.. image-sg:: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_006.png
:alt: Predicted: 0.3703 | Actual: 1.0000
:srcset: /auto_examples/3_models/images/sphx_glr_plot_4_xgb1_reg_006.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 8.159 seconds)
.. _sphx_glr_download_auto_examples_3_models_plot_4_xgb1_reg.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/selfexplainml/piml-toolbox/main?urlpath=lab/tree/./docs/_build/html/notebooks/auto_examples/3_models/plot_4_xgb1_reg.ipynb
:alt: Launch binder
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_4_xgb1_reg.ipynb <plot_4_xgb1_reg.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_4_xgb1_reg.py <plot_4_xgb1_reg.py>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_