B-Analytics/diPLSlib
Python implementation of domain-invariant partial least squares regression (di-PLS)
This tool helps scientists and engineers build robust calibration models for analytical instruments. It takes measurement data from an instrument (X_source) and corresponding reference values (y) and creates a model that can predict outcomes even when the instrument or measurement conditions change over time or across different setups. This is particularly useful for maintaining model accuracy without needing to recalibrate from scratch every time your instrument's environment shifts.
No commits in the last 6 months.
Use this if you need to build or maintain prediction models for analytical measurements where the instrument or sample properties might subtly change over time or between different laboratories, and you want to ensure your model remains accurate.
Not ideal if your measurement conditions are perfectly stable and consistent, or if you are not dealing with multivariate calibration data.
Stars
23
Forks
14
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
May 08, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/B-Analytics/diPLSlib"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
uxlfoundation/scikit-learn-intelex
Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
INRIA/scikit-learn-mooc
Machine learning in Python with scikit-learn MOOC
ddbourgin/numpy-ml
Machine learning, in numpy
nubank/fklearn
fklearn: Functional Machine Learning
gavinkhung/machine-learning-visualized
ML algorithms implemented and derived from first-principles in Jupyter Notebooks and NumPy