LASSO performs feature selection by shrinking the coefficients of variables that have no effect on the response all the way to zero and thus eliminating them from the model. This is referred to as a sparse solution.

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- Categories: Regularization
- Updated: March 26, 2023

LASSO performs feature selection by shrinking the coefficients of variables that have no effect on the response all the way to zero and thus eliminating them from the model. This is referred to as a sparse solution.

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (113)
- Unsupervised Learning (55)
- Deep Learning (23)
- Data Preparation (37)
- General (7)
- Standardization (6)
- Missing data (7)
- Textual Data (17)