Using Synthetic Data: SMOTE and Beyond #ai #artificialintelligence #machinelearning #aiagent #Using

@genaiexp Synthetic Minority Over-sampling Technique (SMOTE) is a popular method for generating synthetic data to address imbalance in datasets. SMOTE creates synthetic instances of the minority class by interpolating between existing instances. This approach can help improve model accuracy and robustness by providing more diverse examples for training. However, SMOTE is not without limitations. It can introduce noise and doesn't consider the distribution of the majority class, which can result in less realistic synthetic examples. Beyond SMOTE, advanced techniques like Adaptive Synthetic Sampling (ADASYN) and Borderline-SMOTE offer more nuanced approaches by focusing on more difficult-to-classify examples or adjusting the level of synthetic data generation based on learning difficulty.