Semantic Search is an advanced information retrieval technique that interprets the meaning behind user queries, enabling systems to understand intent, context, and relationships between terms. Unlike traditional keyword matching, semantic search uses embeddings, ontologies, and machine learning to deliver more accurate, personalized results—especially for complex or conversational queries where literal term matching fails to capture
SHAP (SHapley Additive exPlanations) is a unified framework for explaining individual predictions of machine learning models by quantifying the contribution of each input feature to a specific output. Based on cooperative game theory and Shapley values—a concept developed by Nobel Prize-winning economist Lloyd Shapley in the 1950s—SHAP provides a mathematically rigorous approach to attribution that
Supervised Learning is a machine learning paradigm where algorithms learn from labeled training data to make predictions on new, unseen data. Unlike unsupervised learning which discovers patterns in unlabeled data, supervised learning uses input-output pairs during training to teach systems how to map new inputs to correct outputs. This approach underlies most practical AI applications
