Trimmed Mean Calculator
Calculate a mean after discarding a stated percentage from each end.
Open toolMean Reciprocal Rank Calculator
Calculate each query’s first relevant rank, reciprocal rank and mean reciprocal rank. Provide Queries: ID | ranked IDs | relevant IDs to compute results directly in your browser. Ranks are one-based. Queries without a relevant returned result contribute zero. Ranked document IDs must be unique per query; query IDs must be unique. Relevant sets may be empty; MRR averages over every supplied query. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
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Enter the inputs, then select Calculate.
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How the Mean Reciprocal Rank Calculator Works & Core Formula
The Mean Reciprocal Rank Calculator evaluates Queries: ID | ranked IDs | relevant IDs in client-side JavaScript using the deterministic rule below and updates the output panel immediately without uploading data to a server.
Calculate each query’s first relevant rank, reciprocal rank and mean reciprocal rank.
Ranks are one-based. Queries without a relevant returned result contribute zero. Ranked document IDs must be unique per query; query IDs must be unique. Relevant sets may be empty; MRR averages over every supplied query. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
Mean Reciprocal Rank Calculator Quick Reference & Input Guide
Review the input parameters, allowed value ranges, and worked reference cases for the Mean Reciprocal Rank Calculator below before running your calculation.
| Parameter / Scenario | Default / Sample Value | Rule or Allowed Range | Output / Usage Note |
|---|---|---|---|
| Input: Queries: ID | ranked IDs | relevant IDs | q1 | a,b,c | b q2 | x,y | z | One query per line; unique query IDs, comma-separated ranked IDs and relevant IDs; at most 1,000 queries. | Required workspace parameter |
| Worked Case 1: Baseline run (Queries: ID | ranked IDs | relevant IDs = ) | Preloaded / test values | Calculate each query’s first relevant rank, reciprocal rank and mean reciprocal rank | Queries: ID | ranked IDs | relevant IDs = q1 | a,b,c | b q2 | x,y | z. Output: q1: First relevant rank 2; reciprocal rank 0.5; q2: No relevant result; reciprocal rank 0; Mean reciprocal rank: 0.25. |
| Worked Case 2: Comparison scenario (Mean Average Precision Calculator) | Preloaded / test values | Calculate each query’s first relevant rank, reciprocal rank and mean reciprocal rank | Queries: ID | ranked IDs | relevant IDs = q1 | a,b | a q2 | c,d | d. Output: q1: First relevant rank 1; reciprocal rank 1; q2: First relevant rank 2; reciprocal rank 0.5; Mean reciprocal rank: 0.75. |
Step-by-Step Guide to Using the Mean Reciprocal Rank Calculator
Follow these three steps to configure Queries: ID | ranked IDs | relevant IDs, verify unit and boundary rules, and export your Mean Reciprocal Rank Calculator output.
Enter the inputs
Start with queries: id | ranked ids | relevant ids. One query per line; unique query IDs, comma-separated ranked IDs and relevant IDs; at most 1,000 queries.
Input and settings
Check units and mode
Ranks are one-based. Queries without a relevant returned result contribute zero. Ranked document IDs must be unique per query; query IDs must be unique. Relevant sets may be empty; MRR averages over every supplied query. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
Tool result
Calculate and save
Select Calculate, check the labelled result, then use Copy result or Download result.txt.
Review the details
Practical Worked Examples for Mean Reciprocal Rank Calculator
These concrete scenarios illustrate how the Mean Reciprocal Rank Calculator processes baseline inputs, comparison checks, and boundary constraints.
Baseline run (Queries: ID | ranked IDs | relevant IDs = )
Queries: ID | ranked IDs | relevant IDs = q1 | a,b,c | b q2 | x,y | z. Output: q1: First relevant rank 2; reciprocal rank 0.5; q2: No relevant result; reciprocal rank 0; Mean reciprocal rank: 0.25.
Comparison scenario (Mean Average Precision Calculator)
Queries: ID | ranked IDs | relevant IDs = q1 | a,b | a q2 | c,d | d. Output: q1: First relevant rank 1; reciprocal rank 1; q2: First relevant rank 2; reciprocal rank 0.5; Mean reciprocal rank: 0.75.
Boundary & unit check (Mean Reciprocal Rank Calculator)
When solving for 'mean reciprocal rank calculator', verify that Queries: ID | ranked IDs | relevant IDs use consistent units and stay within the documented scope: Ranks are one-based. Queries without a relevant returned result contribute zero. Ranked document IDs must be unique per query; query IDs must be unique. Relevant sets may be empty; MRR averages over every supplied query. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
How to Read and Verify the Mean Reciprocal Rank Calculator Output
The output panel reports the exact result derived from Queries: ID | ranked IDs | relevant IDs alongside any unit or formatting notes.
Every calculation follows the rule: Calculate each query’s first relevant rank, reciprocal rank and mean reciprocal rank. Because JavaScript uses IEEE 754 double-precision floating-point arithmetic, numeric outputs display up to 12 significant digits where applicable.
Always cross-check the displayed result against the scope note (Ranks are one-based. Queries without a relevant returned result contribute zero. Ranked document IDs must be unique per query; query IDs must be unique. Relevant sets may be empty; MRR averages over every supplied query. Numeric output uses up to 12 significant digits; floating-point rounding can occur) before applying the value to production specifications, financial models, or lab records.
Scope Limits, Accuracy Assumptions & Related Everyday Math Tools
Review these boundary constraints and validation rules before relying on the Mean Reciprocal Rank Calculator output.
Operational scope: Ranks are one-based. Queries without a relevant returned result contribute zero. Ranked document IDs must be unique per query; query IDs must be unique. Relevant sets may be empty; MRR averages over every supplied query. Numeric output uses up to 12 significant digits; floating-point rounding can occur. Inputs that violate minimum/maximum bounds or required format rules are rejected with a clear diagnostic prompt.
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Open toolFrequently Asked Questions About Mean Reciprocal Rank Calculator
Answers about using Mean Reciprocal Rank Calculator and understanding its results.
What formula or rule does the Mean Reciprocal Rank Calculator use?
The Mean Reciprocal Rank Calculator applies the rule: Calculate each query’s first relevant rank, reciprocal rank and mean reciprocal rank. Enter Queries: ID | ranked IDs | relevant IDs into the workspace and the tool computes the exact output in your browser.
What input parameters and units are required for Mean Reciprocal Rank Calculator?
Yes. Provide Queries: ID | ranked IDs | relevant IDs using the documented units and format shown in the quick reference table above. Preloaded default values let you test the workflow immediately.
What are the accuracy limits and boundary assumptions for Mean Reciprocal Rank Calculator?
Ranks are one-based. Queries without a relevant returned result contribute zero. Ranked document IDs must be unique per query; query IDs must be unique. Relevant sets may be empty; MRR averages over every supplied query. Numeric output uses up to 12 significant digits; floating-point rounding can occur. Confirm that your inputs fall within the allowed ranges before exporting the result.
Is the Mean Reciprocal Rank Calculator free and does it keep my inputs private?
Yes. All calculations for the Mean Reciprocal Rank Calculator execute 100% locally in your browser's JavaScript runtime. No inputs or results are uploaded to any external server.
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