accurately represent your data distribution regularize it to a more bias distribution that in turn has lower variance we know this as the bias-variance tradeoff it's actually very simple right you you have the Ferraris and the Lamborghinis and you want make a model that predicts the accident probability now it just so happens that the Ferrari drivers are a bit more reckless and they do slightly higher accidents and now I train my logistic regression and it tells me okay 6040 cool but now I train my logistic regression with an l1 penalty and I say