Databricks Machine Learning Associate Question 552
Select 4You are training a binary classification model in Databricks using a highly imbalanced dataset where the positive class represents only 5% of the total data. Which of the following techniques can help mitigate the data imbalance issue and improve model performance?
- A
Oversampling the minority class using techniques like SMOTE (Synthetic Minority Oversampling Technique).
- B
Undersampling the majority class to balance the dataset.
- C
Using a weighted loss function to give higher importance to the minority class.
- D
Increasing the number of epochs during training to improve the model's understanding of the minority class.
- E
Collecting more data from the minority class to naturally balance the dataset.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Data imbalance in machine learning can lead to biased models that perform poorly on the minority class. Techniques like oversampling, undersampling, weighted loss functions, and collecting more data from the minority class are effective methods to address this issue. However, increasing the number of epochs does not directly mitigate data imbalance and is not sufficient on its own to improve minority class performance.
- A. Correct.
Oversampling the minority class, such as using SMOTE, generates synthetic samples for the minority class, helping balance the dataset. This is a common and effective technique to address data imbalance.
- B. Correct.
Undersampling the majority class reduces the size of the majority class, making the dataset more balanced. However, it can lead to loss of information if the majority class is significantly reduced.
- C. Correct.
Using a weighted loss function assigns higher penalties to misclassified minority class samples, enabling the model to better learn patterns related to the minority class.
- D. Incorrect.
Increasing the number of epochs does not directly address data imbalance. While it may improve general model performance, it does not specifically help the model focus on the minority class.
- E. Correct.
Collecting more data from the minority class is a natural way to handle imbalance and avoids the potential risks of synthetic data generation or information loss from undersampling.