Thursday, October 14, 2004
David,
We usually use the maximum likelihood to pick the most possible lable. If the label is a subset of a set of enumerate values and we don't know how many elements in that subset, what should we do?
For example, given age, salary, gender and education level, to predict if this person is {happy, rich, smart}. A person could be both rich and happy. But we do not know exactly how many labels we should attach to him/her. We cannot pick the most likely two because we don't know there are exactly two.
I read the "Bayesian logistic regression software" webpage and notice that some thresholds are used. Could you explain a bit more about this? Or could you give us some information?
I think you are describing a multivariate regression problem. Up to now we have looked only at univariate regression models where there is one variable that plays the role of the response variable. In your scenario there are three binary response variables, happy (yes or no), rich (yes or no), and smart (yes or no). So you could build three separate binary classifiers (e.g. using the BBR software or support vector machines or whatever). Even better would be to build a multivariate (aka multilabel) classifer. Neural networks are sometimes used for multilabel problems - Tuesday's class will talk about neural networks. D.
We usually use the maximum likelihood to pick the most possible lable. If the label is a subset of a set of enumerate values and we don't know how many elements in that subset, what should we do?
For example, given age, salary, gender and education level, to predict if this person is {happy, rich, smart}. A person could be both rich and happy. But we do not know exactly how many labels we should attach to him/her. We cannot pick the most likely two because we don't know there are exactly two.
I read the "Bayesian logistic regression software" webpage and notice that some thresholds are used. Could you explain a bit more about this? Or could you give us some information?
I think you are describing a multivariate regression problem. Up to now we have looked only at univariate regression models where there is one variable that plays the role of the response variable. In your scenario there are three binary response variables, happy (yes or no), rich (yes or no), and smart (yes or no). So you could build three separate binary classifiers (e.g. using the BBR software or support vector machines or whatever). Even better would be to build a multivariate (aka multilabel) classifer. Neural networks are sometimes used for multilabel problems - Tuesday's class will talk about neural networks. D.
