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It's lambda - basically the amount of regularization (simplification or 'overfit penalty') to impose. Usually chosen by cross-validation - try every lambda betw
by markovbling 10y ago
It's lambda - basically the amount of regularization (simplification or 'overfit penalty') to impose. Usually chosen by cross-validation - try every lambda between say 0 and 10 in 0.5 increments and choose lambda that gives the model the lowest error cross validation measure...
- achompas 10y agoOP's point (and it's valid) is that the author covers the regularization term but does not explain the task aside from the following throwaway line: > "The goal is to find the model that minimzes (sp) this loss function." Unless we're overloading the word "model" -- which is only going to confuse your intended audience -- this statement is incorrect. We're actually searching for the parameterization \alpha of the model that minimizes the loss function.