What is imbalanced dataset?
A dataset where the number of examples in each category is significantly different
imbalanced dataset explained in plain English
An imbalanced dataset is a collection of data where one or more categories have a much larger number of examples than others, which can affect the performance of machine learning models
Analogy
Imagine trying to learn a new language by reading a book that has 100 pages of one topic and only 1 page of another topic - you would likely become very proficient in the first topic but struggle with the second, which is similar to how a model trained on an imbalanced dataset may perform well on the dominant category but poorly on the others
Example
A dataset of images of animals, where there are 1000 images of dogs, 500 images of cats, but only 10 images of birds - this dataset is imbalanced because there are many more examples of dogs and cats than birds
How is imbalanced dataset used?
Imbalanced datasets are often encountered in real-world applications such as image classification, sentiment analysis, and medical diagnosis, where one class may have a significantly larger number of instances than others
Common misconceptions about imbalanced dataset
One common misconception is that imbalanced datasets are always a problem, but in some cases, the imbalance may be representative of the real-world distribution of the data, and the model should be able to handle it
History
The concept of imbalanced datasets has been recognized as a challenge in machine learning since the early 2000s, with researchers developing various techniques to address the issue, such as oversampling the minority class, undersampling the majority class, and using class weights
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