A very senior Microsoft developer who moved to Google told me that Google works and thinks at a higher level of abstraction than Microsoft. “Google uses Bayesian filtering the way Microsoft uses the if statement,” he said.
There are really two approaches to take in selecting candidates. The first is the approach of the if statement: You form a model of what the candidate ought to do, work out what they ought to know in order to do that, and then you work out the questions to ask (or the features to look for) that demonstrate the candidate knows those things. If they know this and this and this and if they don’t have this bad thing or that bad thing, call them in for an interview (or, if you are interviewing them and they have demonstrated their strength, hire).
The second approach is the classifier approach. Each feature you look for, each question you ask, is associated with a probability. You put them all together and you classify them as interview/no interview or hire/no hire with a certain degree of confidence.
The most important thing about most classifiers is that they can be remarkably naïve and still work. In fact, they often work better when they are naïve. Specifically, they do not attempt to draw a logical connection between the features that best classify candidates and the actual job requirements. Classifiers work by training themselves to recognize the differences that have the greatest statistical relevance to the correct classification.
Link Posted by Tim at February 11, 2008 11:13 PM | TrackBack