<a target="_blank" href="https://www.almabetter.com/bytes/tutorials/data-science/regularization-in-machine-learning">Regularization in machine learning</a> is a technique used to prevent overfitting by adding a penalty term to the model's loss function. This penalty discourages overly complex models that may fit the training data too closely, leading to poor generalization on unseen data. Regularization methods include:
<ol>
<li>L1 Regularization (Lasso): Adds the sum of the absolute values of coefficients to the loss function.
</li>
<li>L2 Regularization (Ridge): Adds the sum of the squared values of coefficients to the loss function.
</li>
<li>Elastic Net: Combines L1 and L2 penalties to leverage their respective benefits.
</li>
</ol>
Regularization helps balance model complexity and performance, improving its ability to generalize to new data.

[Regularization in machine learning](https://www.almabetter.com/bytes/tutorials/data-science/regularization-in-machine-learning) is a technique used to prevent overfitting by adding a penalty term to the model's loss function. This penalty discourages overly complex models that may fit the training data too closely, leading to poor generalization on unseen data. Regularization methods include:

1. **L1 Regularization (Lasso)**: Adds the sum of the absolute values of coefficients to the loss function.
    
2. **L2 Regularization (Ridge)**: Adds the sum of the squared values of coefficients to the loss function.
    
3. **Elastic Net**: Combines L1 and L2 penalties to leverage their respective benefits.
    

Regularization helps balance model complexity and performance, improving its ability to generalize to new data.

Regularization in Machine Learning