On the recordRead 12 Oct 202620 facts
MBPLS
On the record
- Algorithms
- The package provides KERNEL, NIPALS, and UNIPALS for multiblock modeling, as well as SIMPLS for fast predictions.mbpls.readthedocs.io · read 12 Oct 2026
- Block contribution
- The introduction example says MB-PLS provides a measure of how much each X block contributes to predicting Y.mbpls.readthedocs.io · read 12 Oct 2026
- Dependencies
- The installation instructions say some dependencies may require upgrades, naming scikit-learn, numpy, and scipy.mbpls.readthedocs.io · read 12 Oct 2026
- Examples
- The documentation links to real-world Jupyter Notebook examples in the project's GitHub repository.mbpls.readthedocs.io · read 12 Oct 2026
- Installation
- The documentation gives `pip install mbpls` as the Python 3 installation command.mbpls.readthedocs.io · read 12 Oct 2026
- Intended fields
- The repository lists bioinformatics, chemometrics, data fusion, metabolomics, and machine learning among its topics.github.com · read 12 Oct 2026
- Large matrices
- The documentation says its algorithms are optimized for robust performance on large data matrices.mbpls.readthedocs.io · read 12 Oct 2026
- License
- The project repository identifies its license as BSD-3-Clause.github.com · read 12 Oct 2026
- Missing data
- The package documentation says it is designed to handle missing data.mbpls.readthedocs.io · read 12 Oct 2026
- Model methods
- The MBPLS class supports fitting, prediction, transforming data, and plotting model values.mbpls.readthedocs.io · read 12 Oct 2026
- Multiple data blocks
- MBPLS predicts a response vector or matrix from multiple data blocks.mbpls.readthedocs.io · read 12 Oct 2026
- Outcomes
- It supports prediction of univariate or multivariate outcomes.mbpls.readthedocs.io · read 12 Oct 2026
- Plotting
- The quick-start example uses a built-in plot method for exploratory analysis of multiblock PLS models.mbpls.readthedocs.io · read 12 Oct 2026
- Purpose
- MBPLS is a Python package for Partial Least Squares prediction modeling with univariate or multivariate outcomes.mbpls.readthedocs.io · read 12 Oct 2026
- Regression models
- Yesmbpls.readthedocs.io · read 24 Sep 2026
- Scikit-learn API
- The package uses an interface described as compatible with the commonly known Scikit-learn API and its model selection toolbox.mbpls.readthedocs.io · read 12 Oct 2026
- Scikit-learn interface
- The package uses a commonly known Scikit-learn API and its model selection toolbox.mbpls.readthedocs.io · read 12 Oct 2026
- Scripting support
- Yesmbpls.readthedocs.io · read 24 Sep 2026
