Word Embeddings are a technique in NLP that represent words as continuous vectors in a high-dimensional space. These vectors capture semantic and syntactic relationships between words. Word embeddings are useful for tasks such as language translation, sentiment analysis, and document clustering.
Decision Trees are Machine Learning models that use a branching structure to make decisions or predictions. By determining the most important features and creating…