NDCG@5
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
NDCG@5 has 14 facts recorded in Dontopedia across 4 references, with 3 live disagreements.
Mostly:rdf:type(3), is metric for(2), evaluates(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (6)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
calculatesCalculates(1)
- Calculate Metrics Function
ex:calculate-metrics-function
calculatesMetricCalculates Metric(1)
- Calculate Metrics
ex:calculate-metrics
comparesCompares(1)
- Correlation Analysis
ex:correlation-analysis
hasMetricHas Metric(1)
- Correlation Between Ndcg and Map
ex:correlation-between-NDCG-and-MAP
hasMetricTypeHas Metric Type(1)
- Ndcg5 Calculation
ex:ndcg5-calculation
plotsRelationBetweenPlots Relation Between(1)
- Visualize Correlation
ex:visualize_correlation
Other facts (12)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Metric | [1] |
| Rdf:type | Metric Instance | [2] |
| Rdf:type | Metric | [4] |
| Is Metric for | Information Retrieval | [3] |
| Is Metric for | Model Evaluation | [4] |
| Evaluates | ranking quality of top 5 items | [1] |
| Focuses on | top 5 items | [1] |
| Metric Type | ranking quality metric | [1] |
| Normalized | true | [1] |
| Has Rank | 5 | [3] |
| Is Used in | Model Evaluation | [4] |
| Has Version | 5 | [4] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (4)
ctx:claims/beam/f815a6d5-3a79-40fc-bcfc-c90172294821ctx:claims/beam/120de523-8aa9-44e6-a94f-a9f5d853f0a8- full textbeam-chunktext/plain1 KB
doc:beam/120de523-8aa9-44e6-a94f-a9f5d853f0a8Show excerpt
Here's how you can implement the calculation and visualization: ```python import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import ndcg_score, average_precision_score def calculate_metrics(predictions, labels, k_ndcg…
ctx:claims/beam/7c7c4d94-1626-4327-b6b2-b57b1fc421dd- full textbeam-chunktext/plain1 KB
doc:beam/7c7c4d94-1626-4327-b6b2-b57b1fc421ddShow excerpt
num_queries = 1000 num_items = 10 # Generate random predictions and labels predictions = np.random.rand(num_queries, num_items) labels = np.random.randint(0, 2, size=(num_queries, num_items)) # Calculate metrics for each query ndcg_values…
ctx:claims/beam/c283ddcf-9f8d-4ec7-9d61-d2da29ccf741- full textbeam-chunktext/plain1 KB
doc:beam/c283ddcf-9f8d-4ec7-9d61-d2da29ccf741Show excerpt
- The `average_precision_score` function from `sklearn.metrics` calculates MAP. Note that the `k` parameter is used to specify the top k items to consider. - The `visualize_correlation` function plots the correlation between NDCG@5 and MAP@…
See also
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