Binary Classification: The Decision Maker
Definition: A classification task predicting one of two mutually exclusive classes, such as spam/not spam or disease/no disease.
The “Hello World” of ML
Binary classification is the simplest useful AI task. Yes/No. True/False. Hotdog/Not Hotdog.
Vibe Coding Applications
You can use LLMs as “Zero-Shot” binary classifiers for your code logic.
- Feature Flagging: “Read this user comment. Is it toxic? (True/False).”
- Routing: “Read this user query. Is it a billing question? (True/False).”
The Threshold Problem
The model outputs a probability (e.g., 0.75). You need a binary decision (1 or 0).
- Threshold: Usually 0.5.
- Tuning: If you move the threshold to 0.9, you get fewer False Positives (Safe) but more False Negatives (Missed opportunities).
Coding the Vibe
When asking AI to make binary decisions:
- Force JSON: “Return
{'is_toxic': boolean}.” - Provide Criteria: “It is toxic IF it contains profanity OR hate speech.” (Defining the decision boundary).
Expert Insight
Binary classification is the building block of complex logic. A “Chatbot” is just a sequence of binary classifiers: “Is this a greeting?”, “Is this a command?”, “Is this an insult?”. Build your agents by chaining these simple Yes/No decisions.