Why is Principal Component Analysis (PCA) Important?
Principal Component Analysis (PCA) reduces the dimensionality of large data sets by transforming variables into principal components. In catalysis, it helps identify the key factors affecting catalytic performance. For example, PCA can simplify the analysis of high-throughput screening results by highlighting the most influential parameters, such as temperature, pressure, and composition.
