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Predictive Modelling with Rare Events

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Hello all, 

 

I am currently in the process of building a few predictive models to help answer the following questions:

- Which current season ticket holders will renew? 

- Who will buy a hospitality product? 

- Who will buy a match ticket/season ticket? 

 

For the latter two questions, I am suffering from rare event bias. This is because the number of e.g. hospitality purchasers relative to non-purchasers is tiny; around 500 purchasers relative to a population of 1M (average propensity: 0.0005). Using a standard probability threshold of 0.5 leads to a confusion matrix failing to predict any actual historical purchasers. 

 

There are lots of articles around this problem - https://statisticalhorizons.com/logistic-regression-for-rare-events -  but I can't seem to find a specific Alteryx fix. 

 

Does anyone have any comments on best practice and Alteryx specific tips and tricks, which can still lead to sensicle probabilities at the end? Thanks! 

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