Databricks Machine Learning Associate Question 554
Select 4You are training a machine learning model on a highly imbalanced dataset where the minority class is significantly underrepresented. Which of the following methods can help mitigate the impact of data imbalance during model training?
- A
Use oversampling techniques such as SMOTE to generate synthetic samples for the minority class.
- B
Apply class weighting to penalize the model more for misclassifying the minority class.
- C
Randomly undersample the majority class to balance the dataset.
- D
Ignore the imbalance and rely on the model to adjust its predictions automatically.
- E
Use a different evaluation metric, such as F1-score, to account for the imbalance.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Data imbalance can significantly impact model performance, particularly for the minority class. Techniques such as oversampling, class weighting, and undersampling can address the imbalance during training. While evaluation metrics like F1-score do not mitigate imbalance, they help assess the model's effectiveness in such scenarios. Ignoring the imbalance is not recommended as it typically leads to poor performance on the minority class.
- A. Correct.
Oversampling techniques like SMOTE (Synthetic Minority Oversampling Technique) help balance the dataset by generating synthetic samples for the minority class, making it easier for the model to learn patterns in the minority class.
- B. Correct.
Class weighting adjusts the loss function during training to penalize the model more for errors on the minority class, encouraging the model to focus more on that class.
- C. Correct.
Random undersampling removes a portion of the majority class to balance the dataset, though it risks losing important information from the majority class. It is still a viable method to mitigate imbalance.
- D. Incorrect.
Ignoring the imbalance and relying on the model to adjust its predictions automatically is not a reliable approach, as most models are biased toward the majority class in imbalanced datasets.
- E. Correct.
Using metrics like F1-score, which balances precision and recall, helps evaluate model performance more appropriately in imbalanced datasets, though this does not directly mitigate the imbalance during training.