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AdaBoost is an algorithm for taking weak classifiers (performing only slightly better than random chance) and boosting/weighing them such that together they are

by daenz 11y ago

AdaBoost is an algorithm for taking weak classifiers (performing only slightly better than random chance) and boosting/weighing them such that together they are a strong classifier. https://en.wikipedia.org/wiki/AdaBoost https://en.wikipedia.org/wiki/AdaBoost To play the devil's advocate, if the dog alerting to drugs is only one of the classifiers in play (others being the suspects look/act under the influence), and if it's weighed correctly in an officer's mind, it is a key part of the strong classifier.

saint_fiasco 11y ago
Humans don't properly implement AdaBoost on their heads. If a dog alerts an officer, confirmation bias will cause the officer to think "now that I look at him closely, he sure seems to be acting strange..." For bad implementations of adaptive boosting, weak information can be worse than no information.
jqm 11y ago
If we could convince the suspects to repeatedly drive by the same officer and dog n number of times so they could be pulled over and a trial run, that might just work.
shkkmo 11y ago
But if you are going to use that reasoning, then you need to remove the dog from the equation and only judge the officers on their actual success rate, utilizing any method at their disposal. What should this rate be? 50%? Any officer who falls below that rate should be forced to get approval from another officer with a higher success rate? They can then choose to use dogs to supplement certain officer's accuracy without giving them incentive to train the dogs to be "probable cause generators". Using a weak classifier to serve as the basis for getting around a constitutional right is pretty messed up.
- daenz 11y ago
  > Using a weak classifier to serve as the basis for getting around a constitutional right is pretty messed up. I was playing the devil's advocate, in case you missed that part, and my point was that a classifier being weak does not necessarily make it bad, as evidenced by AdaBoost.

by daenz 11y ago

saint_fiasco 11y ago

Humans don't properly implement AdaBoost on their heads. If a dog alerts an officer, confirmation bias will cause the officer to think "now that I look at him closely, he sure seems to be acting strange..." For bad implementations of adaptive boosting, weak information can be worse than no information.

jqm 11y ago

If we could convince the suspects to repeatedly drive by the same officer and dog n number of times so they could be pulled over and a trial run, that might just work.

shkkmo 11y ago

But if you are going to use that reasoning, then you need to remove the dog from the equation and only judge the officers on their actual success rate, utilizing any method at their disposal. What should this rate be? 50%? Any officer who falls below that rate should be forced to get approval from another officer with a higher success rate? They can then choose to use dogs to supplement certain officer's accuracy without giving them incentive to train the dogs to be "probable cause generators". Using a weak classifier to serve as the basis for getting around a constitutional right is pretty messed up.

daenz 11y ago
> Using a weak classifier to serve as the basis for getting around a constitutional right is pretty messed up. I was playing the devil's advocate, in case you missed that part, and my point was that a classifier being weak does not necessarily make it bad, as evidenced by AdaBoost.