CREATE BINS BASED ON MIN,MAX AND INC AND IDENTIFY THE BIN IN WHICH THE RESPONSE VALUE FALLS INTO¶
Creates a set of equal-width bins defined by a minimum value, maximum value, and increment, then identifies which bin a given scalar response value falls into. Use this worker to discretize continuous KPI outputs into labeled ranges for classification, reporting, or downstream branching logic.
When to use¶
Classification: process.
Tagged: binning, classification, discretization, increment, range, scalar, transformation.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Response to classify | responsetoclassify | scalar | — | The scalar response value (e.g., a simulation KPI or measured quantity) to be classified into one of the defined bins; leave unconnected if only bin edges are needed. | |
| Min | min | number | 0 | Lower bound of the binning range (same units as the response value); defaults to 0 — set this to the smallest expected response value. | |
| Max | max | number | 0 | Upper bound of the binning range (same units as the response value); defaults to 0 — must be greater than Min for valid bin generation. | |
| Increment | increment | number | 0 | Width of each bin (same units as the response value); defaults to 0 — set to a positive value that evenly divides the [Min, Max] interval. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| binning_number_output_1 | binning_number_output_1 | text | Text label or index identifying the bin in which the response value falls (e.g., ‘Bin 3 [20, 30)’); returns an out-of-range indicator if the value lies outside [Min, Max]. |
Disciplines¶
- ai_ml.supervised.classification
- data.dataset.transform
- data.statistics
Auto-generated from transformation schema. Worker id: binning_number. Schema hash: 2013b28b393f. Hand-curated docs in workerexamples/ override this page when present.