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In order to clarify the variable selection of Lasso, Lasso is compared with two other variable selection methods AIC and stagewise forward. First, the variable selection of Lasso is compared with that of AIC, it is discovered that Lasso has a wider application range than AIC. The data simulation shows the variable selection of Lasso under orthonormal design is consistent with AIC, Lasso under the orthonormal design can be solved by using the algorithm of stepwise selection, the removed variables of Lasso appear again under nonorthonormal design, the variable selection of Lasso under nonorthonormal design isn&#x2019;t consistent with AIC. We continue to compare between the variable selection of Lasso and forward stagewise. Based on analysis of these research, it is pointed out that the variable selection of Lasso is complexity. The infinite number of parameters enable the design matrix to achieve orthonormalization, so that the solution of Lasso can be found with the algorithm of stepwise selection, this may be the reason for the success of the large model represented by ChatGPT.

Variable Selection of Lasso and Large Model