Precision and recall > .90 on holdout data
📰 Reddit r/datascience
Achieve high precision and recall on holdout data using ML models like XGBoost and elastic net logistic regression
Action Steps
- Run XGBoost and elastic net logistic regression models on a balanced dataset created by undersampling the majority category class
- Test model performance on a raw holdout dataset without sampling or rebalancing
- Evaluate precision and recall metrics on the holdout dataset to ensure they are above 0.90
- Configure hyperparameters to optimize model performance on the holdout dataset
- Compare the performance of different models, such as XGBoost and elastic net logistic regression, to select the best one
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this knowledge to improve model performance on unbalanced datasets
Key Insight
💡 Undersampling the majority category class can help achieve a balanced dataset, but it's essential to test model performance on a raw holdout dataset to ensure high precision and recall
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📈 Achieve high precision and recall on holdout data using XGBoost and elastic net logistic regression! 💡
Key Takeaways
Achieve high precision and recall on holdout data using ML models like XGBoost and elastic net logistic regression
Full Article
I'm running ML models (XGBoost and elastic net logistic regression) predicting a 0/1 outcome in a post period based on pre period observations in a large unbalanced dataset. I've undersampled from the majority category class to achieve a balanced dataset that fits into memory and doesn't take hours to run. I understand sampling can distort precision or recall metrics. However I'm testing model performance on a raw holdout dataset (no sampling or rebalanc
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