In a fraud detection system, you have data with numerous features. You suspect that not all features are relevant, and some may even be redundant. Before feeding the data into a classifier, you want to reduce its dimensionality without losing critical information. Which technique would be apt for this?
- Principal Component Analysis (PCA)
- Support Vector Machines (SVM)
- Breadth-First Search
- Quick Sort
Principal Component Analysis (PCA) is used for dimensionality reduction. It identifies the most significant features in the data, allowing you to reduce dimensionality while retaining critical information. In a fraud detection system, this is valuable for improving model performance.
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