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Dense feature: Difference between revisions
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(Created page with "===Introduction== Machine learning takes advantage of datasets that contain various features which can be utilized to make predictions about an outcome of interest. Features are the individual measurements or attributes assigned to each instance in a dataset; dense features in particular are often employed in this process. ==Definition of Dense Feature== Dense features in machine learning refer to those with a high-dimensional vector representation, where each dimension...") |
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===Introduction== | ===Introduction== | ||
[[Dense feature]]s in [[machine learning]] refer to those with a high-dimensional vector representation, where each dimension is usually either continuous or categorical value. It means that most or all of the values of the feature are nonzero. Dense features are commonly employed in neural networks where they can be processed by multiple layers of neurons for producing predictions. On the other hand, sparse features have a low-dimensional vector representation with many dimensions either zero or missing. | |||
==Example of Dense Feature== | ==Example of Dense Feature== | ||