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Logistic Regression

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.

Training Time:
It's computationally inexpensive and quick to train, making it ideal for rapid prototyping.

Preferred Vectorizers:
Works well with TF-IDF and basic Bag-of-Words models.

Preprocessing Requirements:
Preprocessing steps like lowercasing, punctuation removal, and stop-word filtering are often beneficial.