_______ is a dimensionality reduction technique used to reduce the number of features in a dataset while retaining most of the information.
- K-Means Clustering
- Principal Component Analysis (PCA)
- Random Forest
- Support Vector Machine (SVM)
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while retaining essential information. It is commonly used to improve computational efficiency and remove redundant features.
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