3 ms·
There is no free lunch. Any forced adjustments we make to reduce false positives, will increase the false negatives. It's up to us to decide what trade-off we w
by nileshtrivedi 9y ago
There is no free lunch. Any forced adjustments we make to reduce false positives, will increase the false negatives. It's up to us to decide what trade-off we want and the algorithms will simply adapt to that.
If we believe certain attributes to be irrelevant to a situation, we need to build systems that are completely blind to these attributes. This is how double-blind trials, or scientific peer review works.
- nl 9y agoAny forced adjustments we make to reduce false positives, will increase the false negatives. That's just not true. You can have systematic errors caused by bad training data which can be fixed without an increase in false negatives (otherwise new ML systems would never improve over old ones!) In the physical world a good analogy is crash testing of cars. For decades (until 2011!!), crash test dummies were all based on average sized American males. That led to hugely increased risks for female passengers: the female dummy in the front passenger seat registered a 20 to 40 percent risk of being killed or seriously injured, according to the test data. The average for that class of vehicle is 15 percent. Fixing that problem didn't case any increase in accident risk for men. It's the same in machine learning. There is no free lunch This isn't what the no free lunch theorem[2] says. That says that all optimization methods are theoretically equivalent. [1] https://www.washingtonpost.com/local/trafficandcommuting/female-dummy-makes-her-mark-on-male-dominated-crash-tests/2012/03/07/gIQANBLjaS_story.html https://www.washingtonpost.com/local/trafficandcommuting/fem... [2] https://en.wikipedia.org/wiki/No_free_lunch_theorem https://en.wikipedia.org/wiki/No_free_lunch_theorem