Principal Component Analysis (PCA) involves the process by which principal components are computed, and their role in understanding the data. You can use PCA to reduce the number of variables and avoid multicollinearity, or when you have too many predictors relative to the number of observations.
is PCA a learning machine? PCA: Application in Machine Learning. Principal Component Analysis (PCA) is an unsupervised, non-parametric statistical technique primarily used for dimensionality reduction in machine learning. PCA can also be used to filter noisy datasets, such as image compression.
Subsequently, question is, how do you explain PCA?
Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
What does PCA score mean?
Principal Component
