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Related Questions
- What are some common challenges in ensuring data quality when deploying machine learning models, and how can interpretability techniques help address them?
- Can you describe the process of using saliency maps and feature importance to identify biases and errors in data preprocessing steps?
- How do techniques like partial dependence plots and SHAP values aid in understanding the relationships between inputs and outputs in a model, and what issues might they reveal?
- In what ways can permutation importance and variable importance plots be used to detect missing values, outliers, and correlated features in the data?
- What are the benefits of using feature visualization techniques like PCA, t-SNE, and UMAP in identifying high-dimensional data issues, such as multicollinearity or feature redundancy?
- Can you explain how LIME (Local Interpretable Model-agnostic Explanations) and ANOVA-based feature attribution can be used to uncover interaction effects and non-linear relationships in a model?
- How do you incorporate domain knowledge and business insights into interpretability techniques to identify data quality issues that may be specific to the problem domain?
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