I've been working with Qlik AutoML and I'm dealing with an imbalanced dataset. Are there any best practices or techniques within the tool to address this issue? Through Mcdvoice homepage, customer or participant can give his honest opinion (positive or negative feedback) to improve product quality, service, store quality & norms. https://mcdvoice.support/I'd love to hear your experiences and recommendations.
Since this depends on the business use case, handling imbalanced datasets needs to be addressed before the training data is passed to an AutoML experiment.
We recommend reading this section from the user guide where it references certain techniques such as undersampling.
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