Dimension reduction with PCA in mlr3 pipelines
Principal Component Analysis (PCA) is a dimensionality reduction technique commonly used in bioinformatics to analyze and interpret high-dimensional biological data, such as gene expression data, protein profiles, or genomic data.
The main goal of PCA is to find a lower-dimensional representation of the data while preserving the most important patterns and variability present in the original data. It achieves this by transforming the data into a new set of uncorrelated variables called principal components. These principal components are ordered in such a way that the first component captures the maximum amount of variance in the data, the second component captures the second maximum variance, and so on.
PCA is useful in bioinformatics for various applications, including visualization, as it reduces data to two or three dimensions for use in plots. PCA has an important role in feature selection in ML, as it can be used to select...
