.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/linear_model/plot_ols.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_linear_model_plot_ols.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_linear_model_plot_ols.py:
==============================
Ordinary Least Squares Example
==============================
This example shows how to use the ordinary least squares (OLS) model
called :class:`~sklearn.linear_model.LinearRegression` in scikit-learn.
For this purpose, we use a single feature from the diabetes dataset and try to
predict the diabetes progression using this linear model. We therefore load the
diabetes dataset and split it into training and test sets.
Then, we fit the model on the training set and evaluate its performance on the test
set and finally visualize the results on the test set.
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.. code-block:: Python
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
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Data Loading and Preparation
----------------------------
Load the diabetes dataset. For simplicity, we only keep a single feature in the data.
Then, we split the data and target into training and test sets.
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.. code-block:: Python
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
X, y = load_diabetes(return_X_y=True)
X = X[:, [2]] # Use only one feature
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=20, shuffle=False)
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Linear regression model
-----------------------
We create a linear regression model and fit it on the training data. Note that by
default, an intercept is added to the model. We can control this behavior by setting
the `fit_intercept` parameter.
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.. code-block:: Python
from sklearn.linear_model import LinearRegression
regressor = LinearRegression().fit(X_train, y_train)
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Model evaluation
----------------
We evaluate the model's performance on the test set using the mean squared error
and the coefficient of determination.
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.. code-block:: Python
from sklearn.metrics import mean_squared_error, r2_score
y_pred = regressor.predict(X_test)
print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}")
print(f"Coefficient of determination: {r2_score(y_test, y_pred):.2f}")
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Mean squared error: 2548.07
Coefficient of determination: 0.47
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Plotting the results
--------------------
Finally, we visualize the results on the train and test data.
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.. code-block:: Python
import matplotlib.pyplot as plt
fig, ax = plt.subplots(ncols=2, figsize=(10, 5), sharex=True, sharey=True)
ax[0].scatter(X_train, y_train, label="Train data points")
ax[0].plot(
X_train,
regressor.predict(X_train),
linewidth=3,
color="tab:orange",
label="Model predictions",
)
ax[0].set(xlabel="Feature", ylabel="Target", title="Train set")
ax[0].legend()
ax[1].scatter(X_test, y_test, label="Test data points")
ax[1].plot(X_test, y_pred, linewidth=3, color="tab:orange", label="Model predictions")
ax[1].set(xlabel="Feature", ylabel="Target", title="Test set")
ax[1].legend()
fig.suptitle("Linear Regression")
plt.show()
.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_ols_001.png
:alt: Linear Regression, Train set, Test set
:srcset: /auto_examples/linear_model/images/sphx_glr_plot_ols_001.png
:class: sphx-glr-single-img
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Conclusion
----------
The trained model corresponds to the estimator that minimizes the mean squared error
between the predicted and the true target values on the training data. We therefore
obtain an estimator of the conditional mean of the target given the data.
Note that in higher dimensions, minimizing only the squared error might lead to
overfitting. Therefore, regularization techniques are commonly used to prevent this
issue, such as those implemented in :class:`~sklearn.linear_model.Ridge` or
:class:`~sklearn.linear_model.Lasso`.
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 0.156 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_ols.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_ols.ipynb
:alt: Launch binder
:width: 150 px
.. container:: lite-badge
.. image:: images/jupyterlite_badge_logo.svg
:target: ../../lite/lab/index.html?path=auto_examples/linear_model/plot_ols.ipynb
:alt: Launch JupyterLite
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_ols.ipynb <plot_ols.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_ols.py <plot_ols.py>`
.. container:: sphx-glr-download sphx-glr-download-zip
:download:`Download zipped: plot_ols.zip <plot_ols.zip>`
.. include:: plot_ols.recommendations
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_