English to Russian Tech/Engineering IT (Information Technology) Machine learning

Then, penalize or shrink the large coefficients corresponding with some of the predictor variables. It might help with the *bias variance tradeoff* if certain variables are highly coordinated with each other. You might not want to include them both in the linear regression model as they will have a very high variance, leaving one of them out, might slightly bias your model. In other words, you might lose a little bit of prediction capability, but you'll save a lot on the variants and therefore improve your prediction to error. It can also help with model selection, in certain cases for regular organization techniques like the lasso. It may be computationally demanding on large data sets.
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