3
$\begingroup$

I have a very imbalanced sample in which I am trying to predict probability of a rare event (Out of around 25,000 observations, this event is observed around 30 times) and am reluctant to try over/under-sampling on that directly just because of the degree of imbalance.

Just to illustrate what I had in mind with an example: Let's say I'm trying to classify if a gem is an emerald.

But out of 25,000 samples, I only observe 30 emeralds. However, I have some other green stones like Jade and Peridot which bring up my observations to 300.

Would it be a good idea to determine P(Green Stone) and then P(Emerald | Green Stone) by running a two stage classification.

The latter stage will have imbalance ratio of 1 emerald : 9 non-emeralds, which might be more suitable for balancing.

Would appreciate any thoughts/insights on pursuing this idea.

$\endgroup$

1 Answer 1

0
$\begingroup$

Two-stage / hierarchical classification models are very useful.

Typically, the first stage is binary. It predicts the presence or absence of a low-rate event. If present, the second stage categories the type of event.

It is easier to train the models and typically the predictive ability of the models is higher.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.