Hi-LASSO(High-Dimensional LASSO) can theoretically improves a LASSO model providing better performance of both prediction and feature selection on extremely high-dimensional data. Hi-LASSO alleviates bias introduced from bootstrapping, refines importance scores, improves the performance taking advantage of global oracle property, provides a statistical strategy to determine the number of bootstrapping, and allows tests of significance for feature selection with appropriate distribution. In Hi - LASSO will be applied to Use the pool of the python library to process parallel multiprocessing to reduce the time required for the model.

This study was published in IEEE Access:
Y. Kim, J. Hao, T. Mallavarapu, J. Park, and M. Kang, "Hi-LASSO: High-dimensional LASSO", IEEE Access, vol. 7, pp. 44562-44573, 2019. doi: 10.1109/ACCESS.2019.2909071 


         author = {Kim, Youngsoon and Hao, Jie and Mallavarapu, Tejaswini and Park, Joongyang and Kang, Mingon},
         year = {2019},
         month = {01},
         pages = {44562-44573},
         title = {Hi-LASSO: High-Dimensional LASSO},
         volume = {7},
         journal = {IEEE Access},
         doi = {10.1109/ACCESS.2019.2909071}

: Hi-LASSO package is publicly available under the MIT license

: Additional documentation and Installation guide