Introduction Imbalanced datasets are a common issue in machine learning, especially in real-world applications where the distribution of classes in the data is skewed. For example, in fraud detection systems, fraudulent transactions might make up a very small proportion of all transactions, leading to a highly imbalanced dataset. Such imbalances can severely affect the performance
Introduction In the realm of machine learning, online learning refers to algorithms that learn incrementally, processing one data point at a time. This stands in contrast to batch learning, where the model is trained on the entire dataset at once. Online learning is particularly valuable in situations where the data is too large to be
Introduction Clustering is a type of unsupervised machine learning technique used to group similar data points together. It plays a pivotal role in various machine learning applications, including anomaly detection, data compression, and market segmentation. One of the most powerful clustering algorithms is DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which groups data based