Note
Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder
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:
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],
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")
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 0.888 seconds)
Related examples
Plot multi-class SGD on the iris dataset
Plot the decision surfaces of ensembles of trees on the iris dataset