Introduction Active learning is a machine learning paradigm that is used to solve problems where labeled data is scarce or expensive to obtain. In traditional machine learning, a model is trained on a large, fully labeled dataset. However, in many real-world scenarios, labeling data is time-consuming and expensive, particularly when expert knowledge is required. Active
Introduction Ensemble learning is a powerful concept in machine learning where multiple models (often called “learners”) are combined to improve the overall performance of a model. Instead of relying on a single model, ensemble methods leverage the collective knowledge of several models to achieve better predictive performance, robustness, and generalization. This approach is especially useful
Introduction Gaussian Mixture Models (GMMs) are a popular probabilistic model used for representing a mixture of several Gaussian distributions. GMMs are highly effective for modeling data that exhibits multiple underlying subpopulations, especially in unsupervised learning tasks such as clustering, density estimation, and anomaly detection. They are used to approximate complex, multi-modal distributions, making them a