Solution

Isolation Forest is a specialized technique for anomaly detection, which is crucial for identifying fraudulent patterns in transaction data. This model isolates outliers by randomly selecting and partitioning the data, effectively distinguishing normal transactions from potential fraud. It is particularly efficient for high-dimensional datasets and is adept at recognizing patterns that deviate from expected behaviors, common in fraud detection scenarios. Unlike supervised techniques that require labeled data, Isolation Forest can identify anomalies without prior knowledge of fraud patterns, making it highly suitable for real-time detection in dynamic environments like online transactions. The other options are incorrect because: • K-means Clustering groups data into clusters but does not identify anomalies effectively. • Principal Component Analysis (PCA) is a dimensionality reduction tool and not designed for anomaly detection. • Decision Tree Classifier requires labeled data and may not generalize well to unseen anomalies. • Logistic Regression is best for binary classification but lacks isolation capabilities for anomalies.