Text classification using TF-IDF (Term Frequency-Inverse Document Frequency) and AutoML (Automated Machine Learning) can be a powerful method for automatically identifying and categorizing text data.
TF-IDF is a technique used to extract features from text data to represent the importance of specific words or phrases within a document. It is commonly used in natural language processing and information retrieval tasks.
AutoML, on the other hand, is a method of automating the process of selecting and tuning machine learning models. It can save time and resources by automatically selecting the best model for a given dataset and problem.
Combining TF-IDF with AutoML can be an effective way to automatically classify text data by identifying the most important features and selecting the best model to use for classification.