description:
1. change the backend of Lasso_selection from LassoRegression to LassoLars. there is still some numerical issue when penalty is low.
a. 50x faster than Lasso bisection
b. coeficient path now is available in .Plotting function
2. lasso bisection 30% faster.
3. Multi processing support for DT_selection.
4. Early stopping for XGBoost, Lightgbm and CatBoost.
5. Pine summary.
6. threshold tuner: Binary classification's threshold will be chosen by auc.
7. Model's search space now can be obtained by .detail()
On Going:
0. A reliable tutorial. (including mac)
1. Pine monitor, progress bar and report. experiment setting visualization.
ToDo:
1. barutoSHAP (baruto, shap, barutoshap)
2. using pretty, beautiful, good-looking, precise packages:
a. pca
b. The only OPLS da reliable(compare to others), alive, python implement
https://github.com/Omicometrics/pypls?tab=readme-ov-file
c. https://www.omicsanalyst.ca/docs/Gallery.xhtml
3. fairness learning (mljar, fairlearn)
4. cv std
n. add parameter dict(json or yaml-like)