Lecture 40:- Decision Tree Code

Here's a Python code example using scikit-learn to implement a Decision Tree classifier for a simple classification task:

pythonCopy code
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.metrics import accuracy_score

# Generate synthetic dataset
X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=42)

# 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 and fit the Decision Tree classifier
classifier = DecisionTreeClassifier(random_state=42)
classifier.fit(X_train, y_train)

# Make predictions on the test set
y_pred = classifier.predict(X_test)

# Calculate accuracy on the test set
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

# Visualize the Decision Tree
plt.figure(figsize=(10, 6))
plot_tree(classifier, feature_names=['Feature 1', 'Feature 2'], class_names=['Class 0', 'Class 1'], filled=True)
plt.title('Decision Tree Classifier')
plt.show()

In this implementation, we used scikit-learn to create and train a Decision Tree classifier. The process is as follows:

1. We imported the necessary libraries, including numpy, matplotlib.pyplot, make_classification to generate synthetic data, and DecisionTreeClassifier from sklearn.tree to create the Decision Tree classifier.

2. We generated a synthetic dataset with make_classification consisting of two informative features and two classes.

3. We split the dataset into training and testing sets using train_test_split from sklearn.model_selection.

4. We created an instance of DecisionTreeClassifier as classifier with the default hyperparameters.

5. We fitted the classifier with the training data using classifier.fit(X_train, y_train).

6. We made predictions on the test set using classifier.predict(X_test).

7. We calculated the accuracy of the model on the test set using accuracy_score from sklearn.metrics.

8. Finally, we used matplotlib to visualize the Decision Tree classifier.

Keep in mind that this is a basic implementation of a Decision Tree classifier for a synthetic dataset. In practice, you may need to tune hyperparameters, handle missing data, and preprocess the features for real-world datasets. Additionally, Decision Trees can also be used for regression tasks using DecisionTreeRegressor from scikit-learn.