Undersampling example sentences

"Undersampling" Example Sentences

1. Undersampling the majority class improved the model's performance significantly.
2. The researcher chose undersampling to address the class imbalance.
3. We avoided overfitting by employing undersampling techniques.
4. Undersampling, although effective, can lead to information loss.
5. His presentation focused on the benefits of undersampling in machine learning.
6. The algorithm utilizes undersampling to balance the dataset.
7. Proper undersampling is crucial for accurate predictions.
8. Compared to oversampling, undersampling is computationally less expensive.
9. Random undersampling is a simple yet effective technique.
10. The study investigated different undersampling strategies.
11. We implemented undersampling to reduce the computational burden.
12. Careful consideration should be given to the method of undersampling employed.
13. Undersampling helped to mitigate the bias in the model.
14. The impact of undersampling on model accuracy was significant.
15. Before undersampling, the dataset was heavily skewed.
16. After undersampling, the classifier performed much better.
17. The choice between oversampling and undersampling depends on the dataset.
18. Effective undersampling requires careful consideration of data characteristics.
19. They experimented with various undersampling methods to optimize the model.
20. Undersampling is one approach to handle imbalanced datasets.
21. The paper analyzes the effectiveness of different undersampling algorithms.
22. One drawback of undersampling is the potential loss of valuable information.
23. We found that Tomek links improved our undersampling results.
24. This method uses a combination of oversampling and undersampling.
25. The results demonstrate the advantages of intelligent undersampling techniques.
26. NearMiss is a popular undersampling algorithm.
27. The dataset was pre-processed using undersampling before training.
28. Our model benefited greatly from the strategic undersampling applied.
29. With undersampling, we achieved a higher F1-score.
30. Undersampling can be combined with other data augmentation techniques.
31. They performed undersampling to create a balanced dataset for training.
32. The accuracy increased after undersampling the majority class.
33. Undersampling might not always be the best solution.
34. Careful evaluation is needed to determine the optimal level of undersampling.
35. The experiment compared undersampling with other imbalance handling techniques.
36. For this problem, undersampling proved to be more effective than oversampling.
37. The team decided against undersampling due to potential data loss.
38. Combining oversampling and undersampling often yields superior results.
39. The algorithm's performance improved substantially thanks to undersampling.
40. Robust undersampling techniques are crucial for real-world applications.
41. This research explores new approaches to undersampling for imbalanced data.
42. Undersampling is a preprocessing step in many machine learning pipelines.
43. The article discusses the limitations of undersampling in certain contexts.
44. Advanced undersampling methods strive to minimize information loss.
45. They implemented a custom undersampling algorithm for their specific needs.
46. The effectiveness of undersampling depends heavily on the chosen algorithm.
47. Despite undersampling, some class imbalance remained.
48. Undersampling was a critical component of their winning solution.
49. Further research is needed to refine undersampling strategies.
50. The tutorial covered both oversampling and undersampling techniques.