Description
Report diagonal correct counts separately by label and in total; not a normalized accuracy estimate.
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 diagonal correct counts separately by label and in total; not a normalized accuracy estimate.
When to use this tool
Use this tool to report diagonal correct counts separately by label and in total; not a normalized accuracy estimate.
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.correct, result.totalCorrect. Interpret signs, units, ordering and null values using the rules below.
Return each label’s diagonal cell as its correct value and sum those cells into totalCorrect.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.correct | Per-label diagonal correct support values. |
| result.totalCorrect | Sum of all diagonal correct support values. |
{
"input": {
"matrix": [
[
2,
1
],
[
0,
1
]
],
"labels": [
"a",
"b"
]
},
"output": {
"result": {
"correct": [
{
"label": "a",
"value": 2
},
{
"label": "b",
"value": 1
}
],
"totalCorrect": 3
}
}
}Limitations and assumptions
- These are raw correct counts/weights rather than normalized accuracy or a confidence estimate.
- 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.