![]() Its idea is simple-reduce the dimensionality of a dataset, while preserving as much ‘variability’ (i.e. Many techniques have been developed for this purpose, but principal component analysis (PCA) is one of the oldest and most widely used. ![]() ![]() In order to interpret such datasets, methods are required to drastically reduce their dimensionality in an interpretable way, such that most of the information in the data is preserved. Large datasets are increasingly widespread in many disciplines. Because smaller data sets are easier to explore and visualize and make analyzing data much easier and faster for machine learning algorithms without extraneous variables to process. Reducing the number of variables of a data set naturally comes at the expense of accuracy, but the trick in dimensionality reduction is to trade a little accuracy for simplicity. 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.
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