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Sure! Below is a Python script that demonstrates the concept of Decision Trees using the popular scikit-learn library.
# Import necessary libraries
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
# Load the Iris dataset
data = load_iris()
X = data.data
y = data.target
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create a Decision Tree classifier
clf = DecisionTreeClassifier()
# Train the classifier on the training data
clf.fit(X_train, y_train)
# Make predictions on the testing data
y_pred = clf.predict(X_test)
# Evaluate the model
accuracy = metrics.accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Visualize the Decision Tree
from sklearn import tree
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 8))
tree.plot_tree(clf, feature_names=data.feature_names, class_names=data.target_names, filled=True)
plt.show()
In this script, we first import the necessary libraries: load_iris from sklearn.datasets to load the Iris dataset, train_test_split from sklearn.model_selection to split the dataset into training and testing sets, DecisionTreeClassifier from sklearn.tree to create the Decision Tree classifier, and metrics from sklearn to evaluate the model.
We load the Iris dataset and split it into training and testing sets using a 80:20 split. Then, we create a Decision Tree classifier and train it on the training data. After that, we make predictions on the testing data and evaluate the model using accuracy as the metric.
Finally, we visualize the Decision Tree using tree.plot_tree from sklearn and matplotlib.pyplot. The resulting tree is displayed using a figure.