Regularization Infographic
Our regularization infographic provides a comprehensive overview of an essential machine-learning technique.
Regularization is a technique that helps prevent models from overfitting by introducing constraints into the loss function. For instance, in logistic regression, regularization techniques can be used to optimize the model's performance.
The infographic provides a clear, concise definition of regularization, highlighting its role in balancing overfitting and underfitting. It illustrates how adding noise and improving generalization enhance model performance on new data.
It also gives in-depth look at various regularization methods, including L1 regularization (Lasso regularization) and L2 regularization (Ridge regularization). It explains their formulas and how they influence coefficient adjustment in model training. Additionally, the infographic explores Elastic Net, a method blending Ridge and Lasso regularization, along with a 2-stage regularization process.
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