Why Traditional kNN is Not Suited for Imbalanced Datasets

Traditional kNN is highly sensitive to the hyperparameter k, which can lead to inaccurate predictions on imbalanced datasets. Two techniques to improve kNN are distance-weighted kNN, which weighs neighbors by distance, and dynamically updating k, which adjusts k based on the class distribution within the nearest neighbors. Both methods aim to make kNN more robust and effective for datasets with class imbalance.