Description
Return sparse-feature density before and after added nonzeros.
Feature transformations depend on training-derived parameters, category vocabularies and explicit missing-value conventions. A local formula or token rule does not ensure that fitting, application and validation avoid data leakage.
Return sparse-feature density before and after added nonzeros.
When to use this tool
Use Sparse Feature One Entry Density when checking one supplied feature-transform or encoding component.
How to use the tool and how it works
- Enter rows, columns, nonzero, additionalNonzero. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read density, nextDensity, remainingZeros. Use the output convention below.
Return sparse-feature density before and after added nonzeros. Require positive whole rows/columns and bounded whole counts; divide by rows*columns and report remainingZeros.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| rows | Rows | Required. Safe integer; minimum 1. |
| columns | Columns | Required. Safe integer; minimum 1. |
| nonzero | Nonzero | Required. Safe integer; minimum 0. |
| additionalNonzero | Additional Nonzero | Required. Safe integer; minimum 0. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| density | Density | Required. Finite number. |
| nextDensity | Next Density | Required. Finite number. |
| remainingZeros | Remaining Zeros | Required. Safe integer. |
{
"input": {
"rows": 10,
"columns": 20,
"nonzero": 20,
"additionalNonzero": 1
},
"output": {
"density": 0.1,
"nextDensity": 0.105,
"remainingZeros": 179
}
}Limitations and assumptions
- Counts are supplied rather than inspected in a matrix; duplicate entries, sparse storage and actual values are not validated.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
- Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.
Alternative or Complementary methods
Use a correctly fitted preprocessing pipeline and suitable validation design for complete data transformation and model assessment.
References
Similar or alternative tools
- Minmax Feature Train Test Range Gap
Scale a value against training bounds and report range excess.
- Batch Drift One Feature Mean Gap
Compare a batch mean with a supplied reference mean and standard deviation.