GitHub

Covariance Data Frames for Predictive M/EEG Pipelines

For this purpose, coffeine uses DataFrames to handle multiple covariance matrices alongside scalar features. Vectorization and model composition functions are provided that handle composition of valid ...
GitHub

Tigramite – Causal inference for time series datasets

Update: Tigramite now has a new CausalEffects class that allows to estimate (conditional) causal effects and mediation based on assuming a causal graph. Have a look at the tutorial. Further, Tigramite ...
snmjournals.org

RadShap: An Explanation Tool for Highlighting the Contributions of Multiple Regions of Interest to the Prediction of Radiomic Models

Journal of Nuclear Medicine June 2024, jnumed.124.267434; DOI: https://doi.org/10.2967/jnumed.124.267434 These aggregative approaches raise an interesting question ...
Visual Studio Magazine

Kernel Ridge Regression Using C#

KRR is especially useful when there is limited training data, says Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step tutorial. The goal of a machine learning regression problem ...
C&EN

Pursuit of the Ultimate Regression Model for Samarium(III), Europium(III), and LiCl Using Laser-Induced Fluorescence, Design of Experiments, and a Genetic Algorithm for Feature ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Laser-induced fluorescence spectroscopy, Raman scattering, and partial least squares ...
Frontiers

Assessment of Greenhouse Tomato Anthesis Rate Through Metabolomics Using LASSO Regularized Linear Regression Model

While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic ...