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scikit-learn 1.3.2
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# 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.

    # 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(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()

Total running time of the script: (0 minutes 1.598 seconds)

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