EVRI-thing You Need to Know About How to do Partial Least Squares Regression
April 22, 2021
If Principal Components Analysis (PCA) is the most important method in data science and machine learning then Partial Least Squares (PLS) regression is a close second. It has become ubiquitous for building predictive models from multivariate data in an array of applications to numerous to list. It is also used in PLS Discriminant Analysis (PLS-DA) for sample classification and also for compression in other machine learning methods.
Eigenvector Research Vice-President Neal B. Gallagher will be joined by Staff Scientist and Developer Lyle Lawrence to show you how to do PLS in Eigenvector’s MATLAB based PLS_Toolbox or stand-alone Solo so you can start using this important method to build your own predictive models. In this webinar we’ll discuss:
- Why PLS? Ill-conditioned data and fit versus prediction
- The Analysis interface for PLS
- Preprocessing methods and interface
- Cross validation for choosing number of latent variables (LVs)
- Viewing the PLS model
- Scores and loadings (and weights)
- Applying PLS models to new data
- Saving PLS models
We will include live demos with PLS_Toolbox/Solo so you can see exactly how PLS regression is done.
Register for “EVRI-thing You Need to Know About How to do Partial Least Squares Regression” through your existing Eigenvector account, or create one. The webinar is free, but you must register to attend. Find the webinar near the bottom of the page under the “Purchase” tab.
A question and answer session will follow the webinar. Don’t miss your chance to quiz Neal, Lyle and the rest of the Eigen-Guys about how to do PLS regression.
Reserve your seat today. The webinar will be live on Thursday, April 22, at 7:00am PDT, (that’s 16:00 CEST). We will send you a WebEx invitation the day before the webinar. We hope you can attend live, but if you sign up and can’t make it, we’ll send you a link to view the recording the day after the webinar.