https://github.com/harmanveer-2546/guide-to-regularization

Regularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs when a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen data.
https://github.com/harmanveer-2546/guide-to-regularization

generalization inline l1 l2 matplotlib numpy overfitting overfitting-prevention overfitting-reduced pandas regularization regularization-methods regularization-techniques regularization-to-avoid-overfitting seaborn

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Regularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs when a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen data.

• Host: GitHub
• URL: https://github.com/harmanveer-2546/guide-to-regularization
• Owner: harmanveer-2546
• Created: 2024-09-08T10:29:37.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-09-08T10:37:44.000Z (about 1 year ago)
• Last Synced: 2025-04-05T21:44:51.212Z (6 months ago)
• Topics: generalization, inline, l1, l2, matplotlib, numpy, overfitting, overfitting-prevention, overfitting-reduced, pandas, regularization, regularization-methods, regularization-techniques, regularization-to-avoid-overfitting, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 879 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
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  • Readme: README.md

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# Preventing Overfitting: A Guide to Regularization

Regularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs 

when a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen 

data. This can lead to poor performance on real-world applications.  

By introducing a penalty term to the loss function, regularization discourages models from becoming overly complex.

This penalty term is calculated based on the magnitude of the model's parameters.  

### Common Regularization Techniques:

* L1 Regularization (Lasso): This technique encourages sparsity, meaning many model parameters are driven to zero. This can be useful for feature selection,

     as it can help identify the most important features.  

* L2 Regularization (Ridge): L2 regularization prevents individual parameters from becoming too large, which can help to reduce the variance of the model.  

* Elastic Net: This is a combination of L1 and L2 regularization, which can be useful when both feature selection and reducing variance are important.

### When to Use Regularization:

* Limited Training Data: When you have limited training data, regularization can help prevent overfitting by preventing the model from memorizing the training set.

* High-Dimensional Data: With many features, regularization can help to prevent overfitting by reducing the complexity of the model.

* Preventing Overfitting: Regularization is a general technique for preventing overfitting in various machine learning models.

### Key Benefits of Regularization:

* Improved Generalization: Regularization helps models generalize unseen data better.  

* Reduced Overfitting: It prevents models from becoming too complex and memorizing the training data.  

* Feature Selection: L1 regularization can be used for feature selection.  

* Enhanced Model Stability: Regularization can make models more stable and less sensitive to small changes in the data.