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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.

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

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

fromsklearn.datasetsimport load_iris
iris = load_iris ()

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

importmatplotlib.pyplotasplt
importnumpyasnp
fromsklearn.datasetsimport load_iris
fromsklearn.inspectionimport DecisionBoundaryDisplay
fromsklearn.treeimport 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.asarray (y == i).nonzero()
 plt.scatter (
 X[idx, 0],
 X[idx, 1],
 c=color,
 label=iris.target_names[i],
 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")

Decision surface of decision trees trained on pairs of features

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

fromsklearn.treeimport 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 ()