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Possible Answers:
LASSO.
Last seen on: Mirror Quick Answer List 5-October-2022
Random information on the term “LASSO”:
Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based minimizing the sum of absolute deviations (sum of absolute residuals or sum of absolute errors) or the L1 norm of such values. It is analogous to the least squares technique, except that it is based on absolute values instead of squared values. It attempts to find a function which closely approximates a set of data by minimizing residuals between points generated by the function and corresponding data points. The LAD estimate also arises as the maximum likelihood estimate if the errors have a Laplace distribution. It was introduced in 1757 by Roger Joseph Boscovich.
Suppose that the data set consists of the points (xi, yi) with i = 1, 2, …, n. We want to find a function f such that f ( x i ) ≈ y i . {\displaystyle f(x_{i})\approx y_{i}.}