Given $$ and ${}_0,$ a linear classifier $h$:$x\{-1,0,+1\}$ is a function that outputs $+1$ if $*x+{}_0$ is

positive, $0$ if it is zero, and $-1$ if it is negative. In other words, $h(x)=$sign$(*x+{}_0).$

Basics $1$

$1$ point possible $($graded$)$

As described in the lecture above, $h$ is a linear classifier which is defined by the boundary $*x=0($where

theta is a vector perpendicular to the plane.$)$ The $i$ th training data is $(x^{(i)},y^{(i)}),$ where $x^{(i)}$ is a vector and $y^{(i)}$

is a scalar quantity. If $$ is a vector of the same dimension as $x^{(i)},$ what are $y^{(i)}$ and sign$(*x^{(i)})$ respectively?

output of the classifier $h,$ label

label, dimension of the feature vector

label, distance of the point from the linear classifier

label, output of the classifier $h$