What is Imbalanced Data?
Imbalanced data refers to a situation in which the distribution of classes in a dataset is not equal. In machine learning, this can lead to biased models that favor the majority class and perform poorly on the minority class. Imbalanced data is common in real-world problems, such as fraud detection, where the number of fraudulent transactions is much smaller than the number of non-fraudulent transactions.
Strategies to handle imbalanced data
Here are some strategies you can use to handle imbalanced data:
- Resampling: Modify the dataset by oversampling the minority class or undersampling the majority class to balance the class distribution.
- Cost-sensitive learning: Assign different misclassification costs to the majority and minority classes, forcing the model to pay more attention to the minority class.
- Ensemble methods: Use ensemble techniques, such as bagging or boosting, with a focus on improving the performance on the minority class.
Resources on Imbalanced Data
To learn more about handling imbalanced data, you can explore the following resources:
- Handling Imbalanced Data, a blog post that explains five techniques to handle imbalanced data
- 7 tips to handle imbalanced data, a collection of tips for handling imbalanced data in machine learning projects
- Dealing with imbalanced data, an article that provides an overview of techniques for dealing with imbalanced data in machine learning models
- Saturn Cloud, a platform for optimizing data science workflows and enabling powerful cloud-based solutions