Dimensions

In Data Science, dimensions refer to the measurable characteristics or attributes of a dataset. These characteristics can be quantitative or qualitative and are used to describe the data and provide context for analysis. The number of dimensions in a dataset is determined by the number of attributes or features that are included. For example, a dataset of customer information may include dimensions such as age, gender, income, and location. In machine learning, feature selection is the process of identifying the most relevant dimensions to include in a model, while dimensionality reduction techniques such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to reduce the number of dimensions in a dataset without losing important information.
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