Binning: Numeric to Ordinal

Too many distinct values (or categories) can overcomplicate an analysis — the more of them there are, the harder real patterns are to see and the more fragile later steps become, an effect often called the curse of dimensionality. Reducing a fine-grained number down to a handful of ordered categories is one way to guard against that: a marketing campaign, for instance, doesn't need to know a customer's tea strength preference is exactly 6.4 out of 10 — "regular" is enough to act on. Each point below is one cup of tea's measured strength, on a scale from 1 to 10; here it gets converted into three ordered categories — "weak", "regular", and "strong". Where those category boundaries land is a real decision: drag the two cut lines yourself, or pick a method that computes them automatically.

Drag either cut line directly, or tab to it and use the arrow keys (Home/End jump to the domain's edges), to set the weak/regular and regular/strong boundaries by hand — this switches the method to "Manual" if it wasn't already. Or choose one of the automatic methods below to see where it places the cuts instead. The label above each group shows how many cups currently fall in it.

What this teaches: Turn a 1–10 measurement into ordered categories with different binning methods and see where each method places its cut points. It teaches discretization (converting numeric values to ordinal ones) and how binning guards against the curse of dimensionality at the cost of detail.