Explanation:
The Isolation Forest algorithm is specifically designed for anomaly detection. It operates by isolating anomalies rather than profiling normal data points. This makes it an ideal choice in Spark ML for tasks focused on detecting rare and abnormal instances, such as identifying fraudulent transactions in a dataset.
In a machine learning project aimed at detecting fraudulent transactions within a dataset, which Spark ML algorithm is best suited for identifying rare and abnormal instances?
A
Linear Regression
B
Decision Trees
C
Naive Bayes
D
Isolation Forest
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