Database Reference
In-Depth Information
Using a TF-IDF model
While we often refer to training a TF-IDF model, it is actually a feature extraction process
or transformation rather than a machine learning model. TF-IDF weighting is often used as
a preprocessing step for other models, such as dimensionality reduction, classification, or
regression.
To illustrate the potential uses of TF-IDF weighting, we will explore two examples. The
first is using the TF-IDF vectors to compute document similarity, while the second involves
training a multilabel classification model with the TF-IDF vectors as input features.
