On Wed, Apr 30, 2014 at 10:13 AM, Ben Finney <b...@benfinney.id.au> wrote: > The problem is you won't know *which* 90% is accurate, and which 10% is > inaccurate. This is very different from the glass, where it's evident > which part is good. > > So, I can't see that you have any choice but to say that *any* of the > precision predictions should expect, on average, to be (10 + 1 + …) > percent inaccurate. And you can't know which ones. Is that an acceptable > error rate?
But they're all going to be *at least* as accurate as the algorithm says. A figure of 31.4 will be treated as 1 decimal, even though it might really have been accurate to 4; but a figure of 27.1828 won't be incorrectly reported as having only 2 decimals. ChrisA -- https://mail.python.org/mailman/listinfo/python-list
