Multiblock PLS: Block dependent prediction modeling for Python

Andreas Baum*, Laurent Vermue

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Partial Least Squares (PLS) regression is a statistical method for supervised multivariate analysis. It relates two data blocks X and Y to each other with the aim of establishing a prediction model. When deployed in production, this model can be used to predict an outcome y from a newly measured feature vector x. PLS is popular in chemometrics, process control and other analytic fields, due to its striking advantages, namely the ability to analyze small sample sizes and the ability to handle high-dimensional data with cross-correlated features (where Ordinary Least Squares regression typically fails). In addition, and in contrast to many other machine learning approaches, PLS models can be interpreted using its latent variable structure just like principal components can be interpreted for a PCA analysis.
Original languageEnglish
Article number1190
JournalThe Journal of Open Source Software
Issue number34
Number of pages5
Publication statusPublished - 2019

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