Cosine similarity is a Metric used to measure how similar two vectors are by calculating the cosine of the angle between them. It ranges from -1 to 1, where 1 indicates identical orientation, 0 indicates orthogonality, and -1 indicates opposite orientation. Cosine similarity is commonly used in text analysis, information retrieval, and recommendation systems to compare document similarity, user preferences, or item features.
In binary classification, cosine similarity can be used as a feature to help distinguish between two classes. For instance, in text classification tasks, you might represent documents as vectors using techniques like TF-IDF. By calculating the cosine similarity between a new document and the centroids of the two classes, you can determine which class the document is more similar to. This similarity score can then be used as an input feature in a classification model to improve its decision-making process.