Oversampling is a technique used in machine learning to balance the proportions of class distributions in data. It is a method of duplicating existing observations in the minority class to increase their representation in the dataset.
In datasets with imbalanced class distribution, the model tends to classify all new observations to the majority class. This leads to a biased model that performs poorly on the minority class. Oversampling helps to deal with the problem by creating more samples for the minority class to balance the proportion of classes.
There are different approaches to oversampling, including random oversampling, synthetic minority oversampling technique (SMOTE), and adaptive synthetic sampling (ADASYN). Random oversampling involves duplicating minority class samples randomly to increase their numbers. While SMOTE and ADASYN generate synthetic samples in the minority class using interpolation between original minority class samples. SMOTE generates synthetic samples by interpolating between a minority class and their nearest neighbors from the dataset. ADASYN, on the other hand, uses a density distribution to generate synthetic samples, making it more effective for datasets with high-dimensional features.
The main advantage of oversampling is that it improves model performance on imbalanced data. By balancing the proportions of class distribution, the model can learn from more data in the minority class and produce predictions that are accurate for both minority and majority classes. Additionally, the oversampling technique can be integrated into the machine learning pipeline, along with other techniques like undersampling, cross-validation, and hyper-parameter tuning to achieve better model performance.
However, oversampling may come with disadvantages, especially when synthetic data is introduced in the model. It can lead to overfitting, where the model is too complex and performs poorly on the test set. Also, the synthetic samples may not represent the original distribution properly, leading to bias in predictions on the minority class.
In conclusion, oversampling is a useful technique in machine learning for addressing the problem of class imbalance in data. Its use can lead to improved model performance on the minority class, but it needs to be handled with care to avoid overfitting or introducing bias in predictions. It is crucial to choose the right type of oversampling method for the dataset and evaluate the model’s performance on the test set to ensure its generalization on new data.