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Related Questions
- Can feature engineering techniques such as dimensionality reduction or feature selection improve the interpretability of machine learning models on imbalanced datasets?
- How does the selection of relevant features affect the performance and interpretability of machine learning models on imbalanced datasets?
- Can feature engineering help to reduce the impact of class imbalance on the interpretability of machine learning models?
- What are some common feature engineering techniques used to improve the interpretability of machine learning models on imbalanced datasets?
- How does the choice of feature engineering method impact the interpretability of machine learning models on imbalanced datasets with a large number of features?
- Can feature engineering techniques such as principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) help to improve the interpretability of machine learning models on imbalanced datasets?
- What are the trade-offs between using feature engineering techniques to improve interpretability and the potential loss of information or overfitting on imbalanced datasets?
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