What is regularization in plain english? - Cross Validated Is regularization really ever used to reduce underfitting? In my experience, regularization is applied on a complex sensitive model to reduce complexity sensitvity, but never on a simple insensitive model to increase complexity sensitivity
When should I use lasso vs ridge? - Cross Validated The regularization can also be interpreted as prior in a maximum a posteriori estimation method Under this interpretation, the ridge and the lasso make different assumptions on the class of linear transformation they infer to relate input and output data
machine learning - Why use regularisation in polynomial regression . . . Regularization helps in keeping these coefficients at lower values, hence, the curve is smooth We now have less training points on the curve, more training error, but less test error, means, better generalization (less Overfitting)
neural networks - L2 Regularization Constant - Cross Validated When implementing a neural net (or other learning algorithm) often we want to regularize our parameters $\\theta_i$ via L2 regularization We do this usually by adding a regularization term to the c
How does regularization reduce overfitting? - Cross Validated A common way to reduce overfitting in a machine learning algorithm is to use a regularization term that penalizes large weights (L2) or non-sparse weights (L1) etc How can such regularization reduce
Random Forest - How to handle overfitting - Cross Validated Empirically, I have not found it difficult at all to overfit random forest, guided random forest, regularized random forest, or guided regularized random forest They regularly perform very well in cross validation, but poorly when used with new data due to over fitting I believe it has to do with the type of phenomena being modeled It's not much of a problem when modeling a mechanical
Impact of L1 and L2 regularisation with cross-entropy loss Binary cross-entropy is commonly used for binary classification problems The effect of regularization in this context may include: L1 Regularization: It can still induce sparsity in the weight vectors, promoting some weights to become exactly zero This can be useful for feature selection even in the context of binary cross-entropy loss
Why is Laplace prior producing sparse solutions? I was looking through the literature on regularization, and often see paragraphs that links L2 regulatization with Gaussian prior, and L1 with Laplace centered on zero I know how these priors loo