Use interpretability to improve and debug your ML model

Machine learning interpretability is one of the best tools for model debugging and improvement. Improving your model is one of the many goals that interpretability can help with, see also “Don't be dogmatic about interpretability-by-design versus post-hoc.” Here’s how ML interpretability can make your model better: Thanks for reading Mindful Modeler! Subscribe for free to receive new posts and support my work. Subscribed Identify target leakage. A data pre-processing error might accidentally leak information about the target into a feature. Using feature importance methods you can identify suspiciously important features that might cause the target leakage. Debug the model. An example: Finding errors in the way features are coded. If a feature dependence plot shows an increasing effect, but the feature is known to decrease the outcome, the feature may have the wrong sign. Get ideas for feature engineering. By knowing which features are important, for example, through feature importance

Use interpretability to improve and debug your ML model How I used feature importance to fix my model Christoph Molnar Dec 05, 2023 14 Share Machine learning interpretability is one of the best tools for model debugging and improvement. Improving your model is one of the many goals that interpretability can help with, see also “ Don't be dogmatic about interpretability-by-design versus post-hoc .” Here’s how ML interpretability can make your model better: Thanks for reading Mindful Modeler! Subscribe for free to receive new posts and support my work. Subscribe Identify target leakage . A data

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