Description
Sum aligned explicit subgroup confusion observations by group, preserving one output table per exact group name; no hidden label-axis alignment.
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.
Sum aligned explicit subgroup confusion observations by group, preserving one output table per exact group name; no hidden label-axis alignment.
When to use this tool
Use this tool to sum aligned explicit subgroup confusion observations by group, preserving one output table per exact group name; no hidden label-axis alignment.
How to use the tool and how it works
- Enter groupsJson, labels using the input definitions below. Serialize the JSON text fields in the decoded shape shown below.
- Run the operation. Resolve malformed-input errors before interpreting the result.
- Read result.groups. Interpret signs, units, ordering and null values using the rules below.
Validate every supplied {group,matrix} record against the same square label axes. Group exact names in first-appearance order and sum aligned cells within each group.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| groupsJson | JSON array of {group, matrix} records. group is a string; every nonnegative square matrix uses the same supplied label order. | Text, at most 8,192 UTF-16 code units; the stated grammar and narrower limits 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. |
These decoded examples show the structure of the JSON text fields. Supply each as its corresponding serialized JSON string.
{
"groupsJson": [
{
"group": "x",
"matrix": [
[
2,
1
],
[
0,
1
]
]
},
{
"group": "x",
"matrix": [
[
2,
1
],
[
0,
1
]
]
}
]
}| Output field | Interpretation |
|---|---|
| result | Object containing the fields below. |
| result.groups | First-seen exact group names with summed aligned matrices. |
{
"input": {
"groupsJson": "[{\"group\": \"x\", \"matrix\": [[2, 1], [0, 1]]}, {\"group\": \"x\", \"matrix\": [[2, 1], [0, 1]]}]",
"labels": [
"a",
"b"
]
},
"output": {
"result": {
"groups": [
{
"group": "x",
"matrix": [
[
4,
2
],
[
0,
2
]
]
}
]
}
}
}Limitations and assumptions
- All matrices must already use the same axis order; hidden label alignment, weighting and disjointness of subgroup observations are not checked.
- 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 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.