The correct answer is A. Lasso can be interpreted as least-squares linear regression where weights are regularized with the l1 norm.
Lasso is a statistical method that can be used to estimate the coefficients of a linear regression model. It is a type of regularization method, which means that it penalizes models with large coefficients. This can help to prevent overfitting, which is a problem that can occur when a model is too complex and learns the noise in the data instead of the true relationships between the variables.
Lasso can be interpreted as least-squares linear regression where weights are regularized with the l1 norm. This means that the model is penalized for having large coefficients, and the penalty is proportional to the absolute value of the coefficients. This can help to prevent overfitting by forcing the model to choose simpler models with fewer parameters.
The other options are incorrect. Option B is incorrect because the weights in Lasso do not have a Gaussian prior. Option C is incorrect because the weights in Lasso are regularized with the l1 norm, not the l2 norm. Option D is incorrect because the solution algorithm for Lasso is not necessarily simpler than the solution algorithm for least-squares linear regression.