X-Space is a term used in machine learning and data analysis to describe the multidimensional space where data points and their corresponding features are represented. Each dimension in X-Space corresponds to a specific feature or variable, while the data points represent individual instances in the dataset. The concept of X-Space is crucial for visualizing complex relationships within data and understanding how algorithms learn from it. In many cases, X-Space helps in performing dimensionality reduction techniques, such as PCA (Principal Component Analysis), to simplify data representation while retaining essential information.