# Since I cannot directly view the image, I will assume it is a decision boundary plot for a decision tree.
# The following code will generate a decision boundary plot similar to what might be in the user's image.

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier

# Load the iris dataset
iris = datasets.load_iris()
X = iris.data[:, 2:]  # we only take the last two features for easy visualization.
y = iris.target

# Train a DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)
tree_clf.fit(X, y)

# Create a mesh grid for plotting decision boundary
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                     np.arange(y_min, y_max, 0.1))

# Predict class for each point in the mesh grid
Z = tree_clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

# Plotting the decision boundary
plt.figure(figsize=(8, 6))
plt.contourf(xx, yy, Z, alpha=0.3)

# Plot the training points
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, edgecolor='k')
plt.title("Decision surface of a decision tree using paired features")
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.show()