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[D] Seeking Advice for Achieving a 0.97 Score (alien.top)

submitted 1 year ago by poolyhymnia@alien.top to c/machinelearning@academy.garden

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I'm currently working on a machine learning task on Kaggle, and I'm striving to achieve a minimum of 0.97 score in accuracy. While I've made some progress, I've hit a plateau at 0.91 and can't seem to improve beyond that.

Task Description: I'm working on a classification task where tweets need to be classified into two categories: "Sports" or "Politics." I've used various models, including BERT, and have explored hyperparameter tuning, but I haven't been able to achieve the desired accuracy.

Current State: My best model currently has an accuracy of 0.91. I'm looking for ideas, strategies, and any advice that might help me break through this barrier and achieve a 0.97 accuracy score. I'm open to trying new approaches or techniques, and I'd love to hear from anyone who has experience with similar tasks.

Questions:

Are there specific techniques or approaches you recommend for improving model accuracy?
How can I make the most out of my training data and optimize the model further?
Any insights on feature engineering or data augmentation that could help?

I greatly appreciate any insights or feedback you can provide. Please share your experiences, suggestions, or any resources you think might be helpful.

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[–] ucals@alien.top 1 points 1 year ago

That’s easy: model stacking/ensembling. All winning Kaggle grandmasters use it.

You have one model only, using only one technique.

There are several other classic approaches to NLP classification tasks: Naive Bayes, SVMs, CBOW, etc.

The idea behind model stacking: train different models, each one using a different method. Then, train a meta-model, which uses as features the output of each individual model.

This will significantly improve your score. It’s how people win Kaggle competitions.