What is Imbalanced Data and How to handle it?

Imbalanced data pertains to datasets where the distribution of observations in the target class is uneven. In other words, one class label has a significantly higher number of observations, while the other has a notably lower count.

When one class greatly outnumbers the others in a classification, there is imbalanced data. Machine learning models may become biased in their predictions as a result, favoring the majority class. Techniques like oversampling the minority class or undersampling the majority class are used in resampling to remedy this.

Furthermore, it is possible to evaluate model performance more precisely by substituting other assessment measures, such as precision, recall, or F1-score, for accuracy. To further improve the handling of imbalanced datasets for more reliable and equitable predictions, specialized techniques such as ensemble approaches and the incorporation of synthetic data generation can be used.

A key component of machine learning classification tasks is handling unbalanced data, which is characterized by a skewed class distribution with a considerable overrepresentation of one class over the others. The difficulty posed by this imbalance is that models may exhibit inferior performance due to bias towards the majority class. When faced with uneven settings, the model’s bias is to value accuracy over accurately recognizing occurrences of minority classes.

This problem can be solved by applying specialized strategies like resampling (oversampling minority class, undersampling majority class), utilizing various assessment measures (F1-score, precision, recall), and putting advanced algorithms to work with unbalanced datasets into practice.