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Could any one help me know about different approaches, methods or algorithms to build a model only with positive responses.

Let's assume we have a set of customers with a 'positive' behaviour. We want to find customers with a similar profile in the database and that are more likely to have this positive behaviour in the future. This can't be modeled with 0/1 target variable because there's not a clear definition of a 'negative' behaviour.

It looks like a look-alike modeling. However I think that look-alike modeling assumes knowing the response (0/1) for the given set of people (treatment group) and finding similar people in the control group to predict their responses.

Thanks in advance of your response!

MK

Majdi
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1 Answers1

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This sounds like a one-class or unary classification problem where you can build a model of normality using the class that you have.

Take a look at novelty and outlier detection documentation for scikit learn. They discuss the one-class SVM there, which attempts to model the decision boundary given the observations that you have. The example that is implemented is pretty straight forward. Sadly, this implementation does not provide you with a probabilistic measure (although, there are papers on how to add this functionality).

Alternatively, you can consider an auto-associative neural network (replicator network).

There is a nice discussion on time-series anomaly detection here on DS.

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