Logistic Regression is an ideal choice for simpler chatbots focused on straightforward intent recognition, offering quick training times and compatibility with basic vectorizers like TF-IDF.
Use Case for Chatbots:
Logistic Regression is a go-to classifier for simpler, less data-intensive chatbot applications, where the aim is largely around straightforward intent recognition.
Training Data Needed:
Generally performs well with small datasets, needing around 10-20 samples per intent for reliable performance.
It's computationally inexpensive and quick to train, making it ideal for rapid prototyping.
Works well with TF-IDF and basic Bag-of-Words models.
Preprocessing steps like lowercasing, punctuation removal, and stop-word filtering are often beneficial.
Updated about 1 month ago