I encountered the issue of an imbalanced dataset while working on Kaggle. An imbalanced dataset can affect the performance of a machine-learning model. In this blog post, we'll explore what imbalanced datasets are and share strategies and techniques to handle them effectively.
What is an imbalanced dataset?
An imbalanced dataset occurs when one class in your dataset has far fewer instances than the other(s). This often happens in real-world scenarios. For example, in medical diagnosis, the number of healthy patients can greatly outnumber those with a particular condition.
Challenges of an imbalanced dataset?
Working with imbalanced datasets comes with several challenges:
Bias: Machine learning models trained on imbalanced data can be biased toward the majority class, making them less effective at predicting the minority class.
Misleading Accuracy: Accuracy alone can be misleading. A model that predicts the majority class every time might have a high accuracy, but it's practically useless.
Handling imbalanced datasets ?
Data Augmentation: Augment the minority class data with additional information. In image classification, this could involve rotating, cropping, or applying filters to existing images to generate new data.
Choosing the right metric: Choose evaluation metrics that are sensitive to imbalanced datasets such as precision, recall, F1-score, or area under the ROC curve (AUC-ROC), instead of relying solely on accuracy.
Algorithm selection: Choose algorithms that handle imbalanced datasets well. Random Forest, Gradient Boosting, and ensemble methods are often more robust in such scenarios.
Cost-sensitive learning: Modify the learning algorithm to assign misclassification costs, giving higher penalties for misclassifying the minority class.
Collect more data: Collect more data for the minority class. This isn't always an option, but when it is, it can be highly effective.