Implementing text classification

Text classification is of the most widely used paradigms in the field of machine learning and is useful in use cases such as spam detection and email classification and just like any other machine learning algorithm, the workflow is built of Transformers and algorithms. In the field of text processing, preprocessing steps such as stop-word removal, stemming, tokenizing, n-gram extraction, TF-IDF feature weighting come into play. Once the desired processing is complete, the models are trained to classify the documents into two or more classes.

Binary classification is the of inputting two output classes such as spam/not spam and a given credit card transaction is fraudulent or not. Multiclass classification can generate multiple output classes such as hot, cold, freezing, and rainy. There is another technique called Multilabel classification, which can generate multiple labels such as speed, safety, and fuel efficiency can be produced from descriptions of...