Feature Hashing for Text Classification: Reducing Dimensionality and Overfitting

The basic purpose of doing Feature Hashing in text classification is to reduce the dimensionality of text data by transforming it into a fixed-length vector representation. Feature Hashing is a technique that maps the input features to a fixed-length vector by using a hash function. This technique is useful when dealing with large amounts of text data, as it can significantly reduce the memory required to store the data and the computational resources needed to process it. Additionally, Feature Hashing can help to mitigate the problem of overfitting, which can occur when the number of features is too large compared to the number of training examples.