Machine Learning / Classification / Nearest-neighbour classification TEACHING COLLECTION Nearest-neighbour classification Understand neighbour voting and validate k on a meaningful scale.
Helpful prior knowledge: Macro F1 and imbalance · Learn a scale from training rows . These links are guidance, not locks.
Follow teaches the idea; Change asks you to make a choice; Transfer changes the context. Apply, reviews and checkpoints appear where the existing lessons provide them. Go Further lessons are optional.
Lessons in this model deck Start with the first core lesson →
C08 Core · teaching
Neighbour voting: Query point, Nearby votes, Chosen label n1: (0.3, 0.2), class B n2: (0.5, 0.2), class A n3: (0.4, 0.5), class A n4: (1, 0.4), class B n5: (-0.9, 0.5), class B n6: (1.5, 1.2), class A n7: (-1.5, -0.2), class A n8: (2.4, 0), class B n9: (2.9, 0.9), class A Query point Nearby votes Chosen label k = 3: 2 A + 1 B Neighbour voting Explain a classifier’s local distance-based decision.
25–40 min · 4 practices C09 Core · teaching
Validate k on a meaningful scale: Small k, Large k, Validate k Small k Large k Validate k Validate k on a meaningful scale Choose k with fold-local scaling.
12–18 min · 3 practices Browse all Classification lessons, reviews and checkpoints →
Choose an independent workflow challenge →