An imbalanced dataset occurs when the classes in a classification problem are not represented equally. For example, if you're building a model to detect fraud, you might find that 95% of the data points are legitimate transactions, while only 5% are fraudulent. This imbalance can lead to a model that performs well on the majority class but poorly on the minority class because it gets biased toward predicting the majority class. This often results in high accuracy but low precision and recall for the minority class, which can be problematic depending on the application.
To correct an imbalanced dataset, there are several strategies you can employ. One common approach is resampling the dataset. There are two main techniques: oversampling the minority class and undersampling the majority class. Oversampling means adding more copies of the minority class examples, which can help the model learn better representations of those cases. You can use techniques like SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic examples rather than merely duplicating existing ones. On the other hand, undersampling reduces the number of majority class instances, which can help balance the dataset but runs the risk of losing important information.
Another approach is to modify the algorithm itself. Some classification algorithms allow you to specify weights for each class, which can help to penalize errors on the minority class more than on the majority class. This way, the model places more emphasis on getting the minority class predictions right. Techniques such as ensemble learning, where multiple models are combined, can also improve performance on imbalanced datasets. Implementing these strategies can lead to a more balanced approach, improving your model’s ability to generalize across both classes.