Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty to the model's cost function. It improves generalization and reduces overfitting. There are different types of regularization, including L1, L2, and Elastic Net. Regularization can be used in various machine learning models, such as linear regression, logistic regression, decision trees, and neural networks.
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Unveiling Regularization: Nurturing Models for GeneralizationIntroduction:Understanding Regularization:What is Regularization?Types of Regularization:1. L1 Regularization (Lasso):2. L2 Regularization (Ridge):3. Elastic Net Regularization:Linking Regularization to the Slope (m) in y = mx + b:Why Replace Slope (m) with λ * m²:Pros and Cons of Regularization:Pros:Cons:Conclusion:PlainEnglish.io 🚀16 Impressions