Data Science Q&A — (14) Sampling techniques for imbalanced data

Questions marked with an asterisk (*) can be used as general interview questions.

Questions marked with a plus sign (+) can be used as specific technical questions.

(*) Q1. What is class imbalance in supervised learning?

Answer: Class imbalance occurs when the target variable in a dataset has a skewed distribution, where one class is significantly under-represented compared to others. This can lead to models being biased towards the majority class, resulting in poor performance on the minority class.

(*) Q2. Why is class imbalance problematic for machine learning models

Answer: Class imbalance can cause models to be biased towards the majority class, leading to skewed predictions and poor generalization of the minority class. This can result in models failing to effectively identify or classify the minority class, which is often critical in applications like fraud detection.

(*) Q3. What are under-sampling and over-sampling techniques in the context of imbalanced data?

Answer: Under-sampling involves reducing the number of instances in the majority class to match the minority class, while over-sampling involves increasing the number of instances in the minority class to balance the dataset. Both techniques aim to address class imbalance but have different strengths and weaknesses.

(*) Q4. What are hybrid approaches in handling class imbalance?

Answer: Hybrid approaches combine both under-sampling and over-sampling techniques to achieve a more balanced dataset. These methods leverage the strengths of both strategies to improve model performance while minimizing the drawbacks associated with each technique.

(*) Q5. What is a ROC curve, and what does it measure?

Answer: A ROC (Receiver Operating Characteristic) curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR) across different classification thresholds. It measures the model’s ability to distinguish between positive and negative classes.

(*) Q6. Why might a ROC curve not be a good measure of model performance in imbalanced datasets?

Answer: In imbalanced datasets, the ROC curve can be misleading because it evaluates the model’s performance on both majority and minority classes. The ROC curve is heavily influenced by the majority class, which can result in an overly optimistic view of model performance if the model performs poorly on the minority class.

(*) Q7. What are some limitations of using the AUC (Area Under the Curve) as a performance metric in imbalanced data?

Answer: The AUC can be misleading in imbalanced datasets because it reflects the model’s ability to distinguish between classes, but it may not emphasize the performance of the minority class. A high AUC might not necessarily indicate good performance in the minority class, which is often of primary concern in such datasets.

(*) Q8. What is a Precision-Recall (PR) curve, and how does it differ from an ROC curve?

Answer: A Precision-Recall (PR) curve plots Precision against Recall for various threshold settings. Unlike the ROC curve, which plots TPR against FPR, the PR curve focuses on the performance of the model specifically with respect to the minority class, making it more informative for imbalanced datasets.

(*) Q9. Why might Precision-Recall (PR) curves provide a better assessment of model performance in imbalanced datasets?

Answer: PR curves provide a better view of model performance on the minority class because they emphasize Precision and Recall. Precision-recall curves are more sensitive to the performance of the model on the minority class, which is crucial in imbalanced datasets where the positive class is of primary interest.

(*) Q10. What is the F1-score, and why is it useful in evaluating models for imbalanced datasets?

Answer: The F1-score is the harmonic mean of Precision and Recall. It provides a single metric that balances both Precision and Recall, which is useful when dealing with imbalanced datasets. The F1-score helps assess how well the model performs in terms of both identifying positive cases and minimizing false positives.

(*) Q11. How does the F1-score differ from Precision and Recall individually?

Answer: Precision measures the proportion of true positive predictions out of all positive predictions made by the model, while Recall measures the proportion of actual positive cases correctly identified by the model. The F1 score combines both metrics into a single value, providing a balanced assessment of model performance.

(*) Q12. What is random under-sampling, and what are its advantages and limitations?

Answer: Random under-sampling involves reducing the number of instances in the majority class to balance the class distribution. It is easy to apply but can lead to the loss of potentially valuable data, which might affect the model’s ability to learn important patterns.

(+) Q13. What is NearMiss, and how does it improve upon random under-sampling?

Answer: NearMiss is a technique that selects instances from the majority class that are most relevant to the minority class, reducing the risk of losing crucial information. It computes distances between instances to retain informative examples and improve class balance.

(+) Q14. Explain the Condensed Nearest Neighbor Rule (CNN) and its purpose.

Answer: The Condensed Nearest Neighbor Rule (CNN) iteratively refines the training set by moving misclassified instances from a separate dataset to the training set. This process helps to maintain the model’s accuracy while reducing redundancy and preserving important data.

(+) Q15. What is the Edited Nearest Neighbor Rule (ENN), and how does it work?

Answer: The Edited Nearest Neighbor Rule (ENN) removes instances from the majority class that differ from the class of at least two of their three nearest neighbors. This helps to clean up the majority class and improve the clarity of the decision boundary.

(+) Q16. What is the Neighborhood Cleaning Rule (NCL), and how does it address class imbalance?

Answer:** The Neighborhood Cleaning Rule (NCL) uses the Edited Nearest Neighbor (ENN) method to refine the dataset. It identifies instances whose nearest neighbors predominantly belong to the other class and removes those instances, improving the quality of the training data.

(+) Q17. What is Cluster-Based Under-Sampling, and why might it be preferred over random under-sampling?

Answer: Cluster-based under-sampling uses clustering techniques to group similar instances and select representative samples from each cluster. This approach helps preserve the diversity of the majority class and maintains dataset representativeness, which can be more effective than random under-sampling.

(+) Q18. What are Tomek Links, and how do they help improve data quality in imbalanced datasets?

Answer:** Tomek Links are pairs of instances where one is from the majority class and the other from the minority class, and they are each other’s nearest neighbors. Removing instances from Tomek Links helps to reduce class overlap and improve the decision boundary between classes.

(*) Q19. What is Random Oversampling, and what are its potential drawbacks?

Answer: Random Oversampling involves duplicating instances from the minority class to balance the class distribution. While it increases the representation of the minority class, it carries a risk of overfitting since it does not introduce new information and can lead to the memorization of repeated examples.

(+) Q20. How does the Synthetic Minority Over-sampling Technique (SMOTE) work?

Answer: SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances and their nearest neighbors. This technique creates new, non-duplicate examples to help balance the class distribution and improve model performance.

(+) Q21. What is ADASYN, and how does it differ from SMOTE?

Answer: ADASYN (Adaptive Synthetic Sampling) generates synthetic samples based on the density distribution of existing data. It focuses on creating more samples in regions with sparse minority class instances, whereas SMOTE generates synthetic samples uniformly across the feature space.

(+) Q22. How does ADASYN’s density-based approach benefit model training in imbalanced datasets?

Answer: ADASYN’s density-based approach creates synthetic samples in regions where the minority class is underrepresented, improving model performance on challenging examples and providing better training data for areas that are harder to learn.

(*) Q23. What is the importance of handling imbalanced datasets in supervised learning?

Answer: Handling imbalanced datasets is crucial for developing effective models that perform well on both majority and minority classes. It helps to prevent bias towards the majority class and ensures that the model can accurately identify and classify the minority class.

(*) Q24. Why might Precision and Recall be more informative than ROC and AUC in imbalanced datasets?

Answer: Precision and Recall provide more specific insights into the model’s performance on the minority class, which is often of primary interest in imbalanced datasets. ROC and AUC may not emphasize the performance on the minority class as effectively, making Precision and Recall more informative metrics.

(*) Q25. How can under-sampling techniques impact model performance?

Answer: Under-sampling techniques can balance class distributions but may also lead to the loss of valuable data and potential reduction in model performance. Techniques like NearMiss and CNN aim to mitigate this issue by retaining informative examples and reducing redundancy.

(*) Q26. How can over-sampling techniques like SMOTE and ADASYN improve model performance?

Answer: Over-sampling techniques like SMOTE and ADASYN increase the representation of the minority class by generating synthetic samples, which helps the model learn more about the minority class and improve its performance on imbalanced datasets.

Handbook of Anomaly Detection: Cutting-edge Methods and Hands-On Code Examples, 2nd edition

• Handbook of Anomaly Detection — (0) Preface
• Handbook of Anomaly Detection — (1) Introduction
• Data Science Q&A — (1) Anomaly Detection
• Handbook of Anomaly Detection — (2) HBOS
• Data Science Q&A — (2) HBOS
• Handbook of Anomaly Detection — (3) ECOD
• Data science Q&A — (3) ECOD
• Handbook of Anomaly Detection — (4) Isolation Forest
• Data Science Q&A — (4) Isolation Forest
• Handbook of Anomaly Detection — (5) PCA
• Data Science Q&A — (5) PCA
• Handbook of Anomaly Detection — (6) One-Class SVM
• Data Science Q&A — (6) One-class SVM
• Handbook of Anomaly Detection — (7) GMM
• Data Science Q&A — (7) GMM
• Handbook of Anomaly Detection — (8) KNN
• Data science Q&A — (8) KNN
• Handbook of Anomaly Detection — (9) Local Outlier Factor (LOF)
• Data Science Q&A — (9) LOF
• Handbook of Anomaly Detection — (10) Cluster-Based Local Outlier Factor (CBLOF)
• Data Science Q&A — (10) CBLOF
• Handbook of Anomaly Detection — (11) Autoencoders
• Data Science Q&A — (11) Autoencoders
• Handbook of Anomaly Detection — (12) Supervised Learning Primer
• Data science Q&A — (12) Supervised learning primer
• Handbook of Anomaly Detection — (13) Regularization
• Data science Q&A — (13) Regularization
• Handbook of Anomaly Detection — (14) Sampling Techniques for Extremely Imbalanced Data
• Data Science Q&A — (14) Sampling techniques for imbalanced data
• Handbook of Anomaly Detection — (15) Representation Learning for Outlier Detection
• Data science Q&A — (15) Representation Learning for Outlier Detection
• Handbook of Anomaly Detection — (X) Instructor’s manual