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.. "auto_examples/neighbors/plot_classification.py"
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.. only:: html

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

.. _sphx_glr_auto_examples_neighbors_plot_classification.py:


================================
Nearest Neighbors Classification
================================

This example shows how to use :class:`~sklearn.neighbors.KNeighborsClassifier`.
We train such a classifier on the iris dataset and observe the difference of the
decision boundary obtained with regards to the parameter `weights`.

.. GENERATED FROM PYTHON SOURCE LINES 10-14

.. code-block:: Python


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








.. GENERATED FROM PYTHON SOURCE LINES 15-20

Load the data
-------------

In this example, we use the iris dataset. We split the data into a train and test
dataset.

.. GENERATED FROM PYTHON SOURCE LINES 20-28

.. code-block:: Python

    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split

    iris = load_iris(as_frame=True)
    X = iris.data[["sepal length (cm)", "sepal width (cm)"]]
    y = iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)








.. GENERATED FROM PYTHON SOURCE LINES 29-41

K-nearest neighbors classifier
------------------------------

We want to use a k-nearest neighbors classifier considering a neighborhood of 11 data
points. Since our k-nearest neighbors model uses euclidean distance to find the
nearest neighbors, it is therefore important to scale the data beforehand. Refer to
the example entitled
:ref:`sphx_glr_auto_examples_preprocessing_plot_scaling_importance.py` for more
detailed information.

Thus, we use a :class:`~sklearn.pipeline.Pipeline` to chain a scaler before to use
our classifier.

.. GENERATED FROM PYTHON SOURCE LINES 41-49

.. code-block:: Python

    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler

    clf = Pipeline(
        steps=[("scaler", StandardScaler()), ("knn", KNeighborsClassifier(n_neighbors=11))]
    )








.. GENERATED FROM PYTHON SOURCE LINES 50-56

Decision boundary
-----------------

Now, we fit two classifiers with different values of the parameter
`weights`. We plot the decision boundary of each classifier as well as the original
dataset to observe the difference.

.. GENERATED FROM PYTHON SOURCE LINES 56-88

.. code-block:: Python

    import matplotlib.pyplot as plt

    from sklearn.inspection import DecisionBoundaryDisplay

    _, axs = plt.subplots(ncols=2, figsize=(12, 5))

    for ax, weights in zip(axs, ("uniform", "distance")):
        clf.set_params(knn__weights=weights).fit(X_train, y_train)
        disp = DecisionBoundaryDisplay.from_estimator(
            clf,
            X_test,
            response_method="predict",
            plot_method="pcolormesh",
            xlabel=iris.feature_names[0],
            ylabel=iris.feature_names[1],
            shading="auto",
            alpha=0.5,
            ax=ax,
        )
        scatter = disp.ax_.scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, edgecolors="k")
        disp.ax_.legend(
            scatter.legend_elements()[0],
            iris.target_names,
            loc="lower left",
            title="Classes",
        )
        _ = disp.ax_.set_title(
            f"3-Class classification\n(k={clf[-1].n_neighbors}, weights={weights!r})"
        )

    plt.show()




.. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_classification_001.png
   :alt: 3-Class classification (k=11, weights='uniform'), 3-Class classification (k=11, weights='distance')
   :srcset: /auto_examples/neighbors/images/sphx_glr_plot_classification_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 89-98

Conclusion
----------

We observe that the parameter `weights` has an impact on the decision boundary. When
`weights="unifom"` all nearest neighbors will have the same impact on the decision.
Whereas when `weights="distance"` the weight given to each neighbor is proportional
to the inverse of the distance from that neighbor to the query point.

In some cases, taking the distance into account might improve the model.


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

   **Total running time of the script:** (0 minutes 0.584 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_classification.py:

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