Multi-dimensional Space

In data science, multi-dimensional space refers to a mathematical concept where data points are represented in a space with more than three dimensions. Each dimension represents a different variable or feature of the data. Multi-dimensional space is often used in machine learning algorithms to find patterns and relationships between variables that are not easily visible in lower dimensions. For example, in image recognition, each pixel of an image can be considered a dimension, and the relationships between these pixels can be analyzed in a multi-dimensional space to identify patterns and objects within the image. Multi-dimensional space is also used in data visualization, where it can be difficult to represent high-dimensional data in a way that is easily understandable to humans. By reducing the dimensionality of the data, it can be visualized in two or three dimensions while still preserving important relationships between variables.
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