Conditioning (Machine Learning)

Conditioning means providing additional information (like an image or sensor readings) to guide a model’s predictions.

Why It’s Used Instead of learning p(x), we learn p(x∣c), where c is context (e.g., visual input).

• This lets the model generate outputs relevant to a specific situation.

✅ “Conditioned on”

• Implies dependence or influence due to past exposure or rules.
• More common in machine learning, probability, or behavioral science.

Examples:

• “The model’s output is conditioned on the input image.”
• “Her behavior was conditioned on past experiences.”

✅ “Conditional on”

• More formal or academic. Often used in policy, economics, or contract language.
• Implies that something will happen only if a condition is true.

Examples:

• “Admission is conditional on passing the entrance exam.”
• “Funding is conditional on performance metrics.”