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Supervised problem types, by the shape of their output
Nine glyphs in three families. Classification: binary picks one of two slots, multi-class one of five, multi-label several of five. Numeric: regression places a point anywhere on a continuous line, ordinal regression places it in one of five ordered buckets, forecasting continues a line past the present into a dashed future. Structured: ranking orders items by descending bar length, sequence labeling tags every position in a row, and structured prediction emits a tree whose parts depend on each other.
What shape is the answer?
Each family differs in what the model is asked to emit — not in how it is trained.
CLASSIFICATION
NUMERIC
STRUCTURED
Binary
one of two
Multi-class
one of many
Multi-label
many of many
Regression
a point on a continuum
Ordinal regression
buckets that have an order
now
Forecasting
the line continues
Ranking
order is the answer
Sequence labeling
one label per position
Structured prediction
parts constrain each other
Nine supervised problem types, drawn by what the model must emit. The training may look alike; the shape of the answer is what differs.