Partial Least Squares Regression Calculator

Enter your data below as comma-separated values with one data point per line. For multiple features, use format x1,x2,...,y. Specify the number of components to use, and the calculator will perform PLS regression and provide statistics about the model quality and variable importance.

Data Input

Format: Each line should contain 1 feature values followed by 1 target value, all comma-separated.

Model Parameters

15

Partial Least Squares (PLS) Regression

Partial Least Squares Regression is a technique that combines features of principal component analysis and multiple regression. Unlike PCR, PLS considers the response variable when finding the latent components, making it more targeted for prediction.

How It Works

PLS works by finding a linear combination of the predictors (latent components) that maximizes the covariance between the predictors and the response variable. The algorithm:

  1. Extracts latent components that capture the variance in both the predictors and the response
  2. Uses these components as predictors in a regression model
  3. Projects the original data onto this new space for prediction

Key parameters include:

  • Number of Components: How many latent components to use in the model

When to Use PLS Regression

Use Partial Least Squares Regression when:

  • You have many correlated predictors (multicollinearity)
  • You want to consider the response variable in the dimensionality reduction
  • You're working with data where the number of predictors exceeds the number of observations
  • You need a model that works well for prediction
  • You're working in fields like chemometrics, spectroscopy, or process control

How to Use This Calculator

Enter your data below as comma-separated values with one data point per line. For multiple features, use format x1,x2,...,y. Specify the number of components to use, and the calculator will perform PLS regression and provide statistics about the model quality and variable importance.