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.
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
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 ...
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.
The learning rate parameter ($\nu \in [0,1]$) in Gradient Boosting shrinks the contribution of each new base model -typically a shallow tree- that is added in the series. It was shown to dramatically
Is regularization a way to ensure regularity? i.e. capturing regularities? Why do ensembling methods like dropout, normalization methods all claim to be doing regularization?
Regularization - penalty for the cost function, L1 as Lasso & L2 as Ridge Cost/Loss Function - L1 as MAE (Mean Absolute Error) and L2 as MSE (Mean Square Error) Are [1] and [2] the same thing? or are these two completely separate practices sharing the same names? (if relevant) what are the similarities and differences between the two?
Compare, for example, a second-order polynomial without regularization to a fourth-order polynomial with it. The latter can posit big coefficients for the third and fourth powers so long as this seems to increase predictive accuracy, according to whatever procedure is used to choose the penalty size for the regularization procedure (probably ...
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...
Does it mean the regularization path is how to select the coordinate that could get faster convergence? I'm a little confused although I have heard about sparsity often. In addition, could you please give a simple description about the existing solutions of LASSO problem?
