# [DLS Course 4 W3] Question about class score calculation

I have a question about class score.
It looks redundant for me.
Why pc is needed?
Cannot model learn directly each class scores?
[bx, by, bh, bw, c1, c2, c3] is not enough?

Hi NobuhitoKurose,

As you can see in the YOLO algorithm lecture, pc serves to distinguish values of [bx, by, bh, bw, c1, c2, c3] that refer to an object from values of [bx, by, bh, bw, c1, c2, c3] that are just noise. The latter occurs when there is no object in the bounding box. During prediction, pc is a probability (i.e. it will unlikely be exactly 0 or 1). Thus, pc is the probability that the values of [bx, by, bh, bw, c1, c2, c3] are relevant and it indicates how relevant these values are, which is why you need a multiplication.

Now the chance of there being an object of type c1 is equal to P(object&c1) = P (object) * P(c1|object). If P(object) is small, this will be a small value which will be suppressed. In calculating P(object&c1) we can therefore take P(object)*P(c1) as the relevant value to be used, as P(c1) is only relevant when there is an object.

I hope this clarifies.