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Related Questions
- Can data preprocessing techniques such as feature scaling and normalization help reduce the impact of biased training data?
- How can data augmentation techniques like rotation and flipping help mitigate the effects of biased training data?
- Can oversampling the minority class and undersampling the majority class help reduce the impact of biased training data?
- How can feature engineering techniques like dimensionality reduction and feature selection help reduce the impact of biased training data?
- Can data preprocessing techniques like handling missing values and outliers help reduce the impact of biased training data?
- How can data augmentation techniques like generating synthetic data and data perturbation help reduce the impact of biased training data?
- Can ensemble methods like bagging and boosting help reduce the impact of biased training data by combining the predictions of multiple models?
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