  Label smoothing (LS) adopts smoothed targets in classification tasks. For
example, in binary classification, instead of the one-hot target $(1,0)^\top$
used in conventional logistic regression (LR), LR with LS (LSLR) uses the
smoothed target $(1-\frac{\alpha}{2},\frac{\alpha}{2})^\top$ with a smoothing
level $\alpha\in(0,1)$, which causes squeezing of values of the logit. Apart
from the common regularization-based interpretation of LS that leads to an
inconsistent probability estimator, we regard LSLR as modifying the loss
function and consistent estimator for probability estimation. In order to study
the significance of each of these two modifications by LSLR, we introduce a
modified LSLR (MLSLR) that uses the same loss function as LSLR and the same
consistent estimator as LR, while not squeezing the logits. For the loss
function modification, we theoretically show that MLSLR with a larger smoothing
level has lower efficiency with correctly-specified models, while it exhibits
higher robustness against model misspecification than LR. Also, for the
modification of the probability estimator, an experimental comparison between
LSLR and MLSLR showed that this modification and squeezing of the logits in
LSLR have negative effects on the probability estimation and classification
performance. The understanding of the properties of LS provided by these
comparisons allows us to propose MLSLR as an improvement over LSLR.
