Given a function u?L2=L2(D,?), where ? is measure on set D, and linear subspace Vn?L2 of dimension n, we show that near-best approximation u in Vn can be computed from near-optimal budget Cn pointwise evaluations u, with C>1 universal constant. The sampling points are drawn according to some random distribution, the by weighted least-squares method, error assessed expected L2 norm. This result ...