Orthogonal Axes
In data science, orthogonal axes refer to a set of axes that are perpendicular to each other. These axes are used to represent different variables or dimensions of a dataset. Orthogonal axes are commonly used in data visualization techniques such as scatter plots and histograms. By using orthogonal axes, it is possible to visualize the relationship between two or more variables in a dataset. This can help in identifying patterns, trends, and outliers in the data. Orthogonal axes are also used in machine learning algorithms such as principal component analysis (PCA) and singular value decomposition (SVD) to reduce the dimensionality of a dataset while preserving the most important information.
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