An imbalanced dataset refers to a dataset where the distribution of classes or labels is non - uniform. This situation arises when there is a substantial disparity in the number of examples across different classes.
For example, take a dataset consisting of animal images, with the task of classifying them as either "cat" or "dog". If the number of cat images in the dataset is far greater than that of dog images, it is regarded as an imbalanced dataset.
When developing machine learning models, dealing with imbalanced datasets can be challenging. Imbalanced datasets may lead to biased predictions and subpar model performance. This is because the model is more likely to be influenced by the majority class, making it less sensitive to the minority class. For example, in the previously mentioned animal classification case, the model might achieve higher accuracy when predicting "cat" but perform poorly when predicting "dog".
There are various approaches to address the issue of imbalanced datasets. These include using weighted loss functions, undersampling the majority class, and oversampling the minority class. Additionally, it is essential to evaluate the model's performance meticulously using metrics designed for imbalanced datasets, such as the F1 score or the area under the precision - recall curve.