What does "Imbalanced Data" mean?
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Imbalanced data occurs when one category or class in a dataset has many more instances than another. This situation can lead to problems when trying to make predictions or classifications because the model might focus too much on the majority class and ignore the minority class.
For example, consider a dataset used to detect fraud in financial transactions. If there are 95 legitimate transactions for every 5 fraudulent ones, the model could learn to just label everything as legitimate to achieve high accuracy. However, this would miss most of the fraud cases.
Why It Matters
Imbalanced data can affect the performance of machine learning models in various fields, such as healthcare, finance, and manufacturing. For instance, in medical diagnosis, a model trained on imbalanced data might fail to identify rare diseases because the majority of the data comes from common conditions.
Solutions
To deal with imbalanced data, several techniques can be used. One common approach is to balance the dataset, either by adding more samples from the minority class or by reducing the samples from the majority class. Another method is to modify the learning algorithm to pay more attention to the minority class.
Employing these strategies can lead to better predictions and improved performance in machine learning tasks, ensuring that important cases are not overlooked.