.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/neighbors/plot_classification.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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_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 0.532 seconds) .. _sphx_glr_download_auto_examples_neighbors_plot_classification.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/main?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_classification.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/neighbors/plot_classification.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_classification.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_classification.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_classification.zip ` .. include:: plot_classification.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_