Is it better to Undersample or oversample?
As far as the illustration goes, it is perfectly understandable that oversampling is better, because you keep all the information in the training dataset. With undersampling you drop a lot of information. Even if this dropped information belongs to the majority class, it is usefull information for a modeling algorithm.
How can I fix my imbalanced data?
7 Techniques to Handle Imbalanced Data
- Use the right evaluation metrics.
- Resample the training set.
- Use K-fold Cross-Validation in the right way.
- Ensemble different resampled datasets.
- Resample with different ratios.
- Cluster the abundant class.
- Design your own models.
Why is oversampling not preferred?
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.
How to overcome an imbalanced dataset using oversampling?
Overcoming an Imbalanced Dataset using Oversampling. How oversampling yielded great results for classifying cases of Sexual Harassment. When it comes to data science, sexual harassment is an imbalanced data problem, meaning there are few (known) instances of harassment in the entire dataset.
Why are more samples discarded when undersampling data?
A reason could indeed be that we trained our classifiers using few samples. In general, the more imbalanced the dataset the more samples will be discarded when undersampling, therefore throwing away potentially useful information.
How to deal with an imbalanced dataset?
An imbalanced data can create problems in the classification task. Before delving into the handling of imbalanced data, we should know the issues that an imbalanced dataset can create. We will take an example of a credit card fraud detection problem to understand an imbalanced dataset and how to handle it in a better way.
How is random undersampling used in imbalanced learning?
Random undersampling involves randomly selecting examples from the majority class and deleting them from the training dataset. In the random under-sampling, the majority class instances are discarded at random until a more balanced distribution is reached. — Page 45, Imbalanced Learning: Foundations, Algorithms, and Applications, 2013