Partial Least Square Regression (PLS)
It is a method which reduces the variables, used to predict, to a smaller set of predictors. These predictors are then used to perform a regression.
When and Why use PLS
1. When there is high collinearity between features.
2. When there are more features than number of samples
Efficient Regression Modeling: PLS regression offers an
efficient way to model relationships between variables in industrial contexts,
providing a robust method for regression modelling.
Probabilistic Learning: In industrial settings, PLS
regression can be formulated as a probabilistic model, enhancing its
applicability and reliability in regression modeling tasks.
Handling Multicollinearity: PLS regression is effective in
handling multicollinearity, a common issue in industrial data, making it a
suitable choice for analyzing complex datasets with correlated variables.
Predictive Accuracy: PLS regression is known for its
predictive accuracy, making it a valuable tool for industrial applications
where accurate predictions are crucial for decision-making.
PLS is particularly useful when the matrix of predictors has more variables than observations and when there is multicollinearity among X values.
The components obtained from PLS regression are built to explain the dependent variables well, while in Principle Component Analysis(PCA) the components are built to describe the independent variables.
What are some real-world applications of PLSR?
- Chemometrics:
PLS regression is widely used in chemometrics for analyzing chemical data
and spectra
- Bioinformatics:
PLS regression is applied in bioinformatics for analyzing high-dimensional
genomic and proteomic data, making it a versatile tool for genomic
analysis
- Sensometrics:
PLS regression finds applications in sensometrics, which involves the
analysis of sensory data, such as in food science and consumer research
- Neuroscience:
PLS regression is utilized in neuroscience for various applications,
including neuroimaging studies
- Anthropology:
PLS regression is used in anthropology for modeling and analyzing complex
data structures in social sciences
- Medicine
and Health Professions: PLS-SEM, a variant of PLS regression, is
employed in fields like healthcare for handling unobservable or latent
variables and analyzing relationships between variables
- Environmental
Sciences: PLS-SEM is also applied in environmental sciences for data
analysis and modeling relationships between observable and latent
variables
- Business
and Management: PLS-SEM is widely used in business, management, and
accounting for multivariate data analysis, combining regression and linear
analysis methodologies
