.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/classification/plot_classifier_comparison.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_classification_plot_classifier_comparison.py: ===================== Classifier comparison ===================== A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets. Particularly in high-dimensional spaces, data can more easily be separated linearly and the simplicity of classifiers such as naive Bayes and linear SVMs might lead to better generalization than is achieved by other classifiers. The plots show training points in solid colors and testing points semi-transparent. The lower right shows the classification accuracy on the test set. .. GENERATED FROM PYTHON SOURCE LINES 21-160 .. image-sg:: /auto_examples/classification/images/sphx_glr_plot_classifier_comparison_001.png :alt: Input data, Nearest Neighbors, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest, Neural Net, AdaBoost, Naive Bayes, QDA :srcset: /auto_examples/classification/images/sphx_glr_plot_classifier_comparison_001.png :class: sphx-glr-single-img .. code-block:: Python # Code source: Gaël Varoquaux # Andreas Müller # Modified for documentation by Jaques Grobler # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import ListedColormap from sklearn.datasets import make_circles, make_classification, make_moons from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF from sklearn.inspection import DecisionBoundaryDisplay from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn.neural_network import MLPClassifier from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier names = [ "Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process", "Decision Tree", "Random Forest", "Neural Net", "AdaBoost", "Naive Bayes", "QDA", ] classifiers = [ KNeighborsClassifier(3), SVC(kernel="linear", C=0.025, random_state=42), SVC(gamma=2, C=1, random_state=42), GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42), DecisionTreeClassifier(max_depth=5, random_state=42), RandomForestClassifier( max_depth=5, n_estimators=10, max_features=1, random_state=42 ), MLPClassifier(alpha=1, max_iter=1000, random_state=42), AdaBoostClassifier(algorithm="SAMME", random_state=42), GaussianNB(), QuadraticDiscriminantAnalysis(), ] X, y = make_classification( n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1 ) rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape) linearly_separable = (X, y) datasets = [ make_moons(noise=0.3, random_state=0), make_circles(noise=0.2, factor=0.5, random_state=1), linearly_separable, ] figure = plt.figure(figsize=(27, 9)) i = 1 # iterate over datasets for ds_cnt, ds in enumerate(datasets): # preprocess dataset, split into training and test part X, y = ds X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=42 ) x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 # just plot the dataset first cm = plt.cm.RdBu cm_bright = ListedColormap(["#FF0000", "#0000FF"]) ax = plt.subplot(len(datasets), len(classifiers) + 1, i) if ds_cnt == 0: ax.set_title("Input data") # Plot the training points ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k") # Plot the testing points ax.scatter( X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k" ) ax.set_xlim(x_min, x_max) ax.set_ylim(y_min, y_max) ax.set_xticks(()) ax.set_yticks(()) i += 1 # iterate over classifiers for name, clf in zip(names, classifiers): ax = plt.subplot(len(datasets), len(classifiers) + 1, i) clf = make_pipeline(StandardScaler(), clf) clf.fit(X_train, y_train) score = clf.score(X_test, y_test) DecisionBoundaryDisplay.from_estimator( clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5 ) # Plot the training points ax.scatter( X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k" ) # Plot the testing points ax.scatter( X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, edgecolors="k", alpha=0.6, ) ax.set_xlim(x_min, x_max) ax.set_ylim(y_min, y_max) ax.set_xticks(()) ax.set_yticks(()) if ds_cnt == 0: ax.set_title(name) ax.text( x_max - 0.3, y_min + 0.3, ("%.2f" % score).lstrip("0"), size=15, horizontalalignment="right", ) i += 1 plt.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.093 seconds) .. _sphx_glr_download_auto_examples_classification_plot_classifier_comparison.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/classification/plot_classifier_comparison.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/classification/plot_classifier_comparison.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_classifier_comparison.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_classifier_comparison.py ` .. include:: plot_classifier_comparison.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_