#StackBounty: #classification #dataset #active-learning How to do Data acquistion focused on improving accuracy on hold-out test set?

Bounty: 50

I have the task of coming up with a model of 95% accuracy for a classification problem. I have training data and a hold-out data set. I have the opportunity to request data of a particular class with desired characteristics to achieve this objective.

What method shall I use to plan the data acquisition through another team? I am currently at 86% accuracy. I use LightGBM for the model development. Would consider parameter tuning and ensemble with XGBoost and TabNet. But I think I need better data to achieve higher accuracy. Feature engineering is also in play.

Also note that it is a multi-class classification problem.


Get this bounty!!!

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