Handling imbalanced datasets to ensure robustness of models is a common challenge in machine learning, particularly in classification tasks where one class significantly outnumbers the other(s).
In Classification tasks, an imbalanced dataset can lead to a model that performs well on the majority class but poorly on the minority class. This is because the model may learn to predict the majority class more often due to its prevalence.
For Regression tasks, handling outliers or data skewness might be necessary.
In ML_Tools see:
Examples
Consider a scenario where you have an imbalanced dataset of resumes, with a majority of male resumes and a minority of female resumes. You want to build a model to predict gender based on resume features.
Strategies to address imbalances
Data-Level Approaches
Resampling Techniques:
- Oversampling: Increase the number of instances in the minority class by duplicating existing samples or generating new ones using techniques like SMOTE (Synthetic Minority Over-sampling Technique).
- Undersampling: Reduce the number of instances in the majority class by randomly removing samples. This can help balance the dataset but may lead to loss of important information.
- Data Augmentation: Apply transformations to existing data to create new samples, which is particularly useful in image data. Techniques include rotation, flipping, scaling, and cropping.
Algorithm-Level Approaches
- Cost-Sensitive Analysis / Cost-Sensitive Learning: Modify the learning algorithm to give more importance to the minority class. This can be done by assigning higher misclassification costs to the minority class during training.1. You have a perfectly balanced dataset but still experience poor classification accuracy. Why might the class separability be the issue?
- Ensemble Methods: Bagging and Boosting: Use ensemble techniques like Random Forests or AdaBoost, which can be adapted to handle class imbalance by adjusting the sample weights or using balanced bootstrap samples.
Evaluation Metrics & Others
- Model Evaluation/Evaluation MetricsUse Appropriate Metrics: Instead of accuracy, use metrics that are more informative for imbalanced datasets, such as precision, recall, F1-score, and the area under the ROC curve (AUC-ROC).
- Anomaly Detection Models: Treat the minority class as anomalies and use anomaly detection techniques to identify them.
- Transfer Learning: Use pre-trained models that have learned features from a balanced dataset, which can be fine-tuned on the imbalanced dataset.