  In online binary classification under \emph{apple tasting} feedback, the
learner only observes the true label if it predicts ``1". First studied by
\cite{helmbold2000apple}, we revisit this classical partial-feedback setting
and study online learnability from a combinatorial perspective. We show that
the Littlestone dimension continues to provide a tight quantitative
characterization of apple tasting in the agnostic setting, closing an open
question posed by \cite{helmbold2000apple}. In addition, we give a new
combinatorial parameter, called the Effective width, that tightly quantifies
the minimax expected mistakes in the realizable setting. As a corollary, we use
the Effective width to establish a \emph{trichotomy} of the minimax expected
number of mistakes in the realizable setting. In particular, we show that in
the realizable setting, the expected number of mistakes of any learner, under
apple tasting feedback, can be $\Theta(1), \Theta(\sqrt{T})$, or $\Theta(T)$.
This is in contrast to the full-information realizable setting where only
$\Theta(1)$ and $\Theta(T)$ are possible.
