  We construct Monte Carlo methods for the $L^2$-approximation in Hilbert
spaces of multivariate functions sampling no more than $n$ function values of
the target function. Their errors catch up with the rate of convergence and the
preasymptotic behavior of the error of any algorithm sampling $n$ pieces of
arbitrary linear information, including function values.
