An important characteristic of Lasso Regression is that it tends to completely eliminate the weights of the least important features (i.e., set them to zero). For example, the dashed line in the right plot on Figure 4-18 (with α = 10-7) looks quadratic, almost linear: all the weights for the high-degree polynomial features are equal to zero. In other words, Lasso Regression automatically performs feature selection and outputs a sparse model (i.e., with few nonzero feature weights).
- 4. Training Models
- from Hands-On Machine Learning with Scikit-Learn and TensorFlow
- Publisher: O'Reilly Media, Inc.
- Released: March 2017
Speciality of Lasso
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