{"version":"1.0","type":"rich","title":"Gist 5dca03bb2559fe2fad4c","description":"The example uses a Jupyter notebook with Python to perform leave-one-out cross-validation and permutation testing for partial least squares (PLS) regression models. It defines a PRESS score function and a grid search to select the optimal number of components, then shuffles the response variable to generate a null distribution of PRESS scores. The code relies on scikit-learn's `PLSRegression`, `LeaveOneOut`, and `cross_validation` modules, along with NumPy and SciPy for statistical calculations, to estimate a p-value for the model's predictive performance.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-5dca03bb2559fe2fad4c?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}