Howtosignalprocessing
- What is the problem with oversampling?
- Why should we use oversampling?
- Is it a good idea to oversample?
- Is oversampling good in machine learning?
What is the problem with oversampling?
the random oversampling may increase the likelihood of occurring overfitting, since it makes exact copies of the minority class examples. In this way, a symbolic classifier, for instance, might construct rules that are apparently accurate, but actually cover one replicated example.
Why should we use oversampling?
Oversampling is the practice of selecting respondents so that some groups make up a larger share of the survey sample than they do in the population. Oversampling small groups can be difficult and costly, but it allows polls to shed light on groups that would otherwise be too small to report on.
Is it a good idea to oversample?
Oversampling is a well-known way to potentially improve models trained on imbalanced data. But it's important to remember that oversampling incorrectly can lead to thinking a model will generalize better than it actually does.
Is oversampling good in machine learning?
Random Oversampling
For Machine Learning algorithms affected by skewed distribution, such as artificial neural networks and SVMs, this is a highly effective technique.
