  We present a novel method of stacking decision trees by projection into an
ordered time split out-of-fold (OOF) one nearest neighbor (1NN) space. The
predictions of these one nearest neighbors are combined through a linear model.
This process is repeated many times and averaged to reduce variance.
Generalized Linear Tree Space Nearest Neighbor (GLTSNN) is competitive with
respect to Mean Squared Error (MSE) compared to Random Forest (RF) on several
publicly available datasets. Some of the theoretical and applied advantages of
GLTSNN are discussed. We conjecture a classifier based upon the GLTSNN would
have an error that is asymptotically bounded by twice the Bayes error rate like
k = 1 Nearest Neighbor.
