Confusion Predicted Support Table

Description

Report predicted column support totals without calculating per-class rates.

A confusion table uses actual labels as rows and predicted labels as columns. Cells can contain occurrence counts or supplied nonnegative weights; the axis label order must stay with the matrix. Row, column and joint normalization have different denominators, and a zero denominator needs an explicit undefined result.

Report predicted column support totals without calculating per-class rates.

When to use this tool

Use this tool to report predicted column support totals without calculating per-class rates.

How to use the tool and how it works

Enter matrix, labels using the input definitions below.
Run the operation. Resolve malformed-input errors before interpreting the result.
Read result.support. Interpret signs, units, ordering and null values using the rules below.

Sum each predicted column across every actual row and return {label,value} records in supplied label order.^[1]

Input field	Meaning and units	Accepted input
matrix	Square nonnegative finite table: matrix[actualIndex][predictedIndex], both axes in labels order.	Dense list of at most 128 rows of at most 128 finite numbers; the operation’s shape constraints also apply.
labels	Distinct label strings defining actual row and predicted column order; score tools require at least one label.	Dense list of at most 128 strings, each at most 8,192 UTF-16 code units.

Output field	Interpretation
result	Object containing the fields below.
result.support	Predicted-label column-sum records {label,value}, in supplied label order.

json

{
  "input": {
    "matrix": [
      [
        2,
        1
      ],
      [
        0,
        1
      ]
    ],
    "labels": [
      "a",
      "b"
    ]
  },
  "output": {
    "result": {
      "support": [
        {
          "label": "a",
          "value": 2
        },
        {
          "label": "b",
          "value": 2
        }
      ]
    }
  }
}

Limitations and assumptions

Support retains the input count/weight units; no per-class rate or accuracy is computed.
Label strings are compared exactly. Confusion values may be nonnegative fractional weights; a count interpretation requires count data. Probability-like score rows use [0,1] entries with sum tolerance 10⁻⁸, without establishing calibration.
Every declared top-level input is required and unknown fields are rejected. Ordinary strings are limited to 8,192 UTF-16 code units and lists to 128 entries, with tighter operation-specific bounds where stated. JSON text has bounded nesting and node counts.
Numeric outputs must be finite and within the absolute safe-integer magnitude; exact larger integers use decimal text. Source positions are zero-based UTF-16 offsets and span ends are exclusive unless a different convention is explicitly stated.

Alternative or Complementary methods

Check label coverage and axis order before comparing tables. Use a separate evaluation design for uncertainty, calibration, operating thresholds and the consequences of real-world decisions.

References

Tool behavior and example: Confusion Predicted Support Table — tool.io

Similar or alternative tools

Confusion Actual Support Table
Report actual row support totals without calculating per-class rates.
Confusion Column Rate Table
Normalize each predicted column independently with null for zero-support columns; no calibrated probability claim.