Description
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.
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.
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.
When to use this tool
Use this tool to 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.
How to use the tool and how it works
- Enter actual, predicted, labels 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 aligned actual/predicted lists and distinct labels containing every observation. Add one per pair to matrix[actualIndex][predictedIndex], keeping supplied axis order.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| actual | Actual label strings, aligned with predicted and optional weights. | Dense list of at most 128 strings, each at most 8,192 UTF-16 code units. |
| predicted | Predicted label strings, aligned with actual. | Dense list of at most 128 strings, each at most 8,192 UTF-16 code units. |
| 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.labels | Label order for both axes of the returned matrix. |
| result.matrix | Returned nonnegative actual-row/predicted-column table. |
{
"input": {
"actual": [
"a",
"a",
"a",
"b"
],
"predicted": [
"a",
"a",
"b",
"b"
],
"labels": [
"a",
"b"
]
},
"output": {
"result": {
"labels": [
"a",
"b"
],
"matrix": [
[
2,
1
],
[
0,
1
]
]
}
}
}Limitations and assumptions
- Every occurrence counts separately; this does not infer sample identity, weighting, calibration or statistical uncertainty.
- 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
- Weighted Label Pair Confusion Table
Build a weighted multiclass confusion table from aligned actual/predicted observations and nonnegative supplied weights; zero weights retained as evidence, no probability inference.
- Confusion Label Axis Reorder
Reorder both axes by a complete explicit label permutation without changing counts or label identity.