PLS Number of Components
Offered By: Chemometrics & Machine Learning in Copenhagen via YouTube
Course Description
Overview
Learn about validating Partial Least Squares (PLS) models in this 26-minute video, focusing on selecting the optimal number of components. Explore the importance of model validation, outlier detection, and regression error measures. Examine predicted versus measured plots and delve into cross-validation techniques, including a detailed look at chemical analyses by six laboratories. Discover a secret trick related to cross-validation and gain insights into rules of thumb for making informed choices when validating PLS models.
Syllabus
Validation of PLS models - always important!
Outliers - why, how and when...?
Regression - Error measures
Predicted versus Measured plot
Cross validation - being smart with segments • Chemical analyses by six laboratories
Cross validation in more detail!
Secret trick - the other thing cross-validation does
Choice is not always simple - A few rules of thumb Rule
Taught by
Chemometrics & Machine Learning in Copenhagen
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