Prove that no linear classifier using word features $($i$.$e$.$ word count $-$ it maps each

word to the number of occurrences of that word in the review$)$ can get zero error on

this dataset. Remember that this is a question about classifiers, not

optimization algorithms; your proof should be true for any linear

classifier of the form $f$\_\{\backslash$mathbf$\{$w$\}\}($x$)=\backslash$text$\{$sign$\}(\backslash$mathbf$\{$w$\}\backslash$cdot $\backslash$phi$($x$))$$$,$ regardless of how the weights are learned.

Propose a single additional feature for your dataset that we could augment the feature vector with that would fix this problem.