Understanding Temperature in Machine Learning Models
Machine Learning (ML) models use a parameter called temperature to control the randomness or creativity of their output. Think of temperature as a “risk dial” — low values make the model play it safe, while high values let it take more chances in its word choices. 📊 Temperature vs. Output Behavior Temperature Range ML Model Output Characteristics 0.1 – 0.3 (Low) • ✅ More predictable and factual • ✅ Deterministic responses 0.7 (Medium) • ⚖️ Balanced creativity and reliability • 🗣️ Natural and varied responses without being too random 1.0 and above (High) • ⚠️ Possible hallucinations • 🎲 High randomness and creativity 🔎 Temperature Examples (Same Prompt, Different Temperatures) Prompt: What is Nmap? ...