What are some applications of PCA in machine learning?
PCA, or Principal Component Analysis, is used for dimensionality reduction, data visualization, and noise reduction in datasets.
Can you give me an example of how PCA is used in image processing?
Sure! In image processing, PCA can reduce the number of features in an image while preserving essential information, making it easier to analyze.
How does PCA help in improving the performance of machine learning models?
By reducing dimensionality, PCA helps eliminate noise and redundant features, which can lead to faster training times and improved model accuracy.
Are there any limitations to using PCA?
Yes, PCA assumes linear relationships and may not perform well with non-linear data. It also can lose some information during reduction.
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