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…
Introduction Metric learning is a subfield of machine learning that focuses on learning a distance function that quantifies the similarity or dissimilarity between data points. Unlike traditional machine learning models that typically use fixed, pre-defined metrics (such as Euclidean distance), metric learning aims to learn the best metric that captures the underlying structure of the…