Regularization Rate
is a parameter that controls the strength of the penalty applied to model complexity during machine learning training. It determines how strongly the model is encouraged to avoid overly complex patterns and overfitting.
What is Regularization Rate?
The regularization rate, often represented by λ (lambda), determines the contribution of the regularization term to a model's training objective. A higher rate generally applies a stronger penalty to large or complex parameter values, while a lower rate allows the model more flexibility.
Why is Regularization Rate Important?
Choosing an appropriate regularization rate helps balance model complexity and generalization. If it is too low, the model may overfit the training data. If it is too high, the model may become overly constrained and underfit the data.
Common use cases
Regularization rates are commonly used when training linear models, logistic regression models, neural networks, and other machine learning systems that use L1 or L2 regularization.