Short Definition

Inductive bias is the set of assumptions a model makes to generalize.

Definition

Inductive bias refers to the assumptions embedded in a model that guide learning beyond the training data. Architecture choice, loss functions, and training procedures all impose inductive bias.

Without inductive bias, generalization is impossible.

Why It Matters

Inductive bias explains why certain models work well for specific tasks, such as CNNs for images or Transformers for language.

How It Works (Conceptually)

• Models restrict hypothesis space
• Assumptions guide pattern selection
• Bias trades flexibility for generalization

Minimal Python Example

# Convolution assumes locality and translation invariance

Common Pitfalls

• Assuming models are unbiased
• Overfitting due to weak inductive bias
• Using the wrong architecture

Related Concepts

• Generalization
• Model Capacity
• Architecture Design
• Bias–Variance Tradeoff