Regularization: Difference between revisions

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#REDIRECT [[Lasso regression]]
[[Category:Machine Learning]]
 
'''Regularization''' is a technique that prevents [[overfit]]ting in machine learning.
 
= Techniques =
 
There are primarily two regularization techniques:
* L1: [[Lasso regression]]
* L2: [[Ridge regression]]
 
The two differ slightly in degree of regularization but has similar principles: applying a penalty term to the loss function to discourage many large weights.
 
= Regularization parameter =
 
The '''regularization parameter''' controls the degree to which the model is regularized; a higher value penalizes large weights more.
 
When the regularization parameter is ''too high'', some weights tend toward zero. The range of regularization parameter depends on the following
* scale of the weights of the model
* magnitude of noise

Revision as of 19:57, 18 May 2024


Regularization is a technique that prevents overfitting in machine learning.

Techniques

There are primarily two regularization techniques:

The two differ slightly in degree of regularization but has similar principles: applying a penalty term to the loss function to discourage many large weights.

Regularization parameter

The regularization parameter controls the degree to which the model is regularized; a higher value penalizes large weights more.

When the regularization parameter is too high, some weights tend toward zero. The range of regularization parameter depends on the following

  • scale of the weights of the model
  • magnitude of noise