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Understanding Principal Component Analysis by singhanshita565 is a document available to read on EtoBox.
Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction by retaining essential information while reducing the number of variables in a dataset. It helps address issues like overfitting and the curse of dimensionality by identifying independent features and simplifying complex data. PCA is widely applicable in fields such as computer vision and bioinformatics, although it is primarily effective with quantitative data.
- Author
- singhanshita565
- Language
- EN