Statistics, Data Science, Analytics, Business Intelligence, Machine Learning.
<- function(pred, actual){ mean((actual - pred)^2) } # while sometimes model_min yields lower MSE # generally model_1se performs better in this example mse(predict(model_1se, X[-train,]), y[-train]) # 0.09685109 mse(predict(model_min, X[-train,]), y[-train]) # 0.1022131 Yeah, you can groan because