Glossary

**XGBoost**: A scalable and efficient implementation of gradient boosting that is widely used for supervised learning tasks, known for its speed and performance in competitions and real-world applications.

**XOR Problem**: A classic problem in machine learning where the exclusive OR (XOR) operation cannot be solved by a simple linear classifier, illustrating the need for more complex models like neural networks.

**XML (eXtensible Markup Language)**: A markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable, often used in data exchange between systems.

**XOR Neural Network**: A neural network that can solve the XOR problem by learning non-linear decision boundaries, typically requiring a hidden layer to capture the XOR relationship.

**XAI (Explainable AI)**: An area of artificial intelligence focused on creating models that are interpretable and understandable by humans, providing insights into how models make decisions, which is crucial for transparency and trust.

**X-Cross Validation**: A term sometimes used to refer to cross-validation, a model validation technique where a dataset is split into multiple folds, and the model is trained and tested multiple times to assess its performance.

**X-Means Clustering**: An extension of k-means clustering that automatically determines the number of clusters by iteratively splitting and merging clusters based on the Bayesian Information Criterion (BIC).

**XOR Gate**: A digital logic gate that outputs true or "1" only when the two binary inputs are different, often used in machine learning to demonstrate non-linear classification problems.

**XOR Encryption**: A simple encryption technique that uses the XOR operation to encrypt data by combining it with a key, often used in cryptography for its simplicity and speed.

**XOR Problem in Neural Networks**: A problem that highlights the limitations of single-layer perceptrons, as they cannot solve the XOR problem, requiring multi-layer networks to learn non-linear decision boundaries.

**X-Shaped Pattern**: A pattern observed in certain datasets or visualizations where data points form an X-like shape, often indicating interactions or correlations between variables in a two-dimensional space.

**X-Fold Cross-Validation**: Another term for k-fold cross-validation, where the dataset is divided into X (k) folds, and the model is trained and tested X times, each time with a different fold as the test set.

**XOR Pattern Recognition**: The task of recognizing XOR patterns in data, often used as a benchmark problem for testing the capability of neural networks to learn non-linear relationships.

**XOR Function**: A function that outputs true only when the inputs are different, commonly used in digital circuits, binary operations, and demonstrating the need for non-linear models in machine learning.

**X-inactivation**: A biological process where one of the X chromosomes in female mammals is randomly inactivated during early development, ensuring dosage compensation for X-linked genes.

**X-ray Image Analysis**: The use of machine learning and computer vision techniques to analyze X-ray images for medical diagnosis, material inspection, and security screening.

**X-Factor**: A variable or element that has a significant but unpredictable influence on the outcome of a process, often used in discussions about uncertainty or innovation in machine learning projects.

**X-ray Imaging**: A technique that uses X-rays to view the internal structure of objects, widely used in medical diagnostics, material science, and security, with machine learning increasingly applied to automate and enhance image analysis.

**XOR Shift**: A type of pseudorandom number generator that uses XOR and bitwise shift operations to produce a sequence of numbers, known for its simplicity and speed in generating random numbers.