This StatQuest episode, part two of a series on Random Forests, delves into how to handle missing data and perform clustering using this powerful machine learning algorithm. It provides an in-depth explanation of these advanced applications.
This content explains K-Means Clustering, an unsupervised learning method for finding groups in unlabeled data. It details the step-by-step algorithm, key concepts like centroids and inertia, and methods for picking the optimal number of clusters (K).
This paper argues that for collective decision-making based on free-form text, embeddings should measure "preferential similarity" rather than "semantic similarity". Existing embeddings capture a coarse preference signal, but fail when this correlation breaks, a problem formalized as an invariance issue.