The correct answer is C. Isolation Forest.
Isolation Forest is a machine learning algorithm that can be used for anomaly detection. It works by isolating instances that are likely to be anomalies by randomly selecting a set of features and then recursively partitioning the data set based on those features. Instances that are isolated early in the process are more likely to be anomalies.
Decision Trees are a type of supervised machine learning algorithm that can be used for classification or regression tasks. They work by recursively splitting the data set into smaller and smaller subsets until each subset contains only one class or value.
K-Means Clustering is a type of unsupervised machine learning algorithm that can be used to cluster data points into groups. It works by randomly selecting k data points as the initial cluster centers and then iteratively assigning each data point to the cluster with the nearest cluster center.
Naive Bayes is a type of supervised machine learning algorithm that can be used for classification tasks. It works by assuming that the features are independent of each other and then using Bayes’ theorem to calculate the probability of each class label given the features.
Anomaly detection is the process of identifying data points that do not conform to the expected behavior of the data set. Anomalies can be caused by errors, fraud, or other unusual events. Isolation Forest is a well-suited algorithm for anomaly detection because it can identify anomalies that are difficult to detect with other algorithms.