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scikit-learn 1.3.2
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# Plot the decision surface of decision trees trained on the iris dataset

Plot the decision surface of a decision tree trained on pairs of features of the iris dataset.

See decision tree for more information on the estimator.

For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples.

We also show the tree structure of a model built on all of the features.

First load the copy of the Iris dataset shipped with scikit-learn:

    from sklearn.datasets import load_iris

    iris = load_iris()

Display the decision functions of trees trained on all pairs of features.

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import load_iris
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.tree import DecisionTreeClassifier

    # Parameters
    n_classes = 3
    plot_colors = "ryb"
    plot_step = 0.02

    for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):
        # We only take the two corresponding features
        X = iris.data[:, pair]
        y = iris.target

        # Train
        clf = DecisionTreeClassifier().fit(X, y)

        # Plot the decision boundary
        ax = plt.subplot(2, 3, pairidx + 1)
        plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
        DecisionBoundaryDisplay.from_estimator(
            clf,
            X,
            cmap=plt.cm.RdYlBu,
            response_method="predict",
            ax=ax,
            xlabel=iris.feature_names[pair[0]],
            ylabel=iris.feature_names[pair[1]],
        )

        # Plot the training points
        for i, color in zip(range(n_classes), plot_colors):
            idx = np.where(y == i)
            plt.scatter(
                X[idx, 0],
                X[idx, 1],
                c=color,
                label=iris.target_names[i],
                cmap=plt.cm.RdYlBu,
                edgecolor="black",
                s=15,
            )

    plt.suptitle("Decision surface of decision trees trained on pairs of features")
    plt.legend(loc="lower right", borderpad=0, handletextpad=0)
    _ = plt.axis("tight")

    /root/scikit-learn-1.3.2/examples/tree/plot_iris_dtc.py:63: UserWarning:

    No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

    /root/scikit-learn-1.3.2/examples/tree/plot_iris_dtc.py:63: UserWarning:

    No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

    /root/scikit-learn-1.3.2/examples/tree/plot_iris_dtc.py:63: UserWarning:

    No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

    /root/scikit-learn-1.3.2/examples/tree/plot_iris_dtc.py:63: UserWarning:

    No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

    /root/scikit-learn-1.3.2/examples/tree/plot_iris_dtc.py:63: UserWarning:

    No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

    /root/scikit-learn-1.3.2/examples/tree/plot_iris_dtc.py:63: UserWarning:

    No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

Display the structure of a single decision tree trained on all the features together.

    from sklearn.tree import plot_tree

    plt.figure()
    clf = DecisionTreeClassifier().fit(iris.data, iris.target)
    plot_tree(clf, filled=True)
    plt.title("Decision tree trained on all the iris features")
    plt.show()

Total running time of the script: (0 minutes 0.517 seconds)

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