inductive-bias-governs-generalization

IN premise — entries/2026/06/21/wiki-Supervised_learning-chunk-3.md

Created 2026-06-21T09:55:53+00:00

Inductive bias is the set of assumptions a learner uses to predict outputs for unseen inputs; every supervised algorithm has one (e.g., linearity in linear regression, axis-aligned splits in decision trees)

Dependents

These beliefs depend on this one:

• IN inductive-bias-not-biological-fidelity-drives-ml — CNNs illustrate that effective ML architectures can succeed through well-chosen inductive biases rather than biological fidelity — their local connectivity and weight sharing capture useful structural constraints despite not faithfully replicating neuroscience. The manifold hypothesis offers one explanation for why such biases work, since if data lies along low-dimensional manifolds, architectures that exploit local structure can generalize effectively regardless of their biological motivation.