87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
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"""
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=======================================================================
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Plot the decision surface of decision trees trained on the iris dataset
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=======================================================================
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Plot the decision surface of a decision tree trained on pairs
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of features of the iris dataset.
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See :ref:`decision tree <tree>` for more information on the estimator.
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For each pair of iris features, the decision tree learns decision
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boundaries made of combinations of simple thresholding rules inferred from
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the training samples.
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We also show the tree structure of a model built on all of the features.
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"""
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# %%
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# First load the copy of the Iris dataset shipped with scikit-learn:
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from sklearn.datasets import load_iris
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iris = load_iris()
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# %%
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# Display the decision functions of trees trained on all pairs of features.
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import load_iris
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.tree import DecisionTreeClassifier
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# Parameters
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n_classes = 3
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plot_colors = "ryb"
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plot_step = 0.02
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for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):
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# We only take the two corresponding features
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X = iris.data[:, pair]
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y = iris.target
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# Train
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clf = DecisionTreeClassifier().fit(X, y)
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# Plot the decision boundary
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ax = plt.subplot(2, 3, pairidx + 1)
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plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
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DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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cmap=plt.cm.RdYlBu,
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response_method="predict",
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ax=ax,
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xlabel=iris.feature_names[pair[0]],
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ylabel=iris.feature_names[pair[1]],
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)
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# Plot the training points
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for i, color in zip(range(n_classes), plot_colors):
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idx = np.where(y == i)
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plt.scatter(
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X[idx, 0],
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X[idx, 1],
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c=color,
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label=iris.target_names[i],
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edgecolor="black",
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s=15,
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)
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plt.suptitle("Decision surface of decision trees trained on pairs of features")
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plt.legend(loc="lower right", borderpad=0, handletextpad=0)
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_ = plt.axis("tight")
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# %%
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# Display the structure of a single decision tree trained on all the features
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# together.
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from sklearn.tree import plot_tree
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plt.figure()
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clf = DecisionTreeClassifier().fit(iris.data, iris.target)
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plot_tree(clf, filled=True)
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plt.title("Decision tree trained on all the iris features")
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plt.show()
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