Description
Reorder both axes by a complete explicit label permutation without changing counts or label identity.
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.
Reorder both axes by a complete explicit label permutation without changing counts or label identity.
When to use this tool
Use this tool to reorder both axes by a complete explicit label permutation without changing counts or label identity.
How to use the tool and how it works
- Enter matrix, labels, order using the input definitions below.
- Run the operation. Resolve malformed-input errors before interpreting the result.
- Read result.labels, result.matrix. Interpret signs, units, ordering and null values using the rules below.
Require order to be a complete unique permutation of the supplied labels. Reindex both actual rows and predicted columns with that same permutation and preserve every value.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. |
| order | Complete distinct permutation of labels for both output axes. | 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.labels | Label order for both axes of the returned matrix. |
| result.matrix | Returned nonnegative actual-row/predicted-column table. |
{
"input": {
"matrix": [
[
2,
1
],
[
0,
1
]
],
"labels": [
"a",
"b"
],
"order": [
"b",
"a"
]
},
"output": {
"result": {
"labels": [
"b",
"a"
],
"matrix": [
[
1,
0
],
[
1,
2
]
]
}
}
}Limitations and assumptions
- Only axis order changes; no class renaming, merging or automatic alignment against another table is performed.
- 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
Similar or alternative tools
- Confusion Label Class Collapse
Collapse both axes through explicit per-label group names, summing every count exactly once; first group appearance defines output order.
- Label Pair Confusion Table
Build an occurrence-count multiclass confusion table over explicit actual/predicted label observations with a caller-ordered complete label universe; unknown labels reject, no identity coercion.