In statistics and machine learning, lasso (least absolute shrinkage and selection operator) (also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. It was introduced by Robert Tibshirani in 1996 based on Leo Breiman’s Nonnegative Garrote. Lasso was originally formulated for least squares models and this simple case reveals a substantial amount about the behavior of the estimator, including its relationship to ridge regression and best subset...
At some point, I bumped into an SO question asking how to implement Perl's trivial hash-of-hashes data structure in Java.
For those who don't know Perl, the hash value can be a scalar or another hash, or technically speaking hash reference. Arbitrary depth. Additional feature offered by Perl is...
