recall_rate
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
recall_rate is The percentage of correct nearest neighbors retrieved..
27 facts·13 predicates·8 sources·2 in dispute
Mostly:rdf:type(8), description(1), measures(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (26)
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.
isMeasuredOnIs Measured on(6)
- Annoy 1 18 0
ex:Annoy-1-18-0 - Faiss 1 7 3
ex:Faiss-1-7-3 - Hnswlib 0 9 2
ex:Hnswlib-0-9-2 - Milvus 2 3 0
ex:Milvus-2-3-0 - Qdrant 0 8 1
ex:Qdrant-0-8-1 - Weaviate 1 19 0
ex:Weaviate-1-19-0
hasMemberHas Member(2)
- Performance Metrics
ex:performance-metrics - Performance Metrics Category
ex:performance-metrics-category
inputToInput to(2)
- False Negatives
ex:false_negatives - True Positives
ex:true_positives
calculatesMetricCalculates Metric(1)
- Vector Search Metrics
ex:vector-search-metrics
containsElementContains Element(1)
- Metrics
ex:metrics
containsMetricContains Metric(1)
- Additional Performance Metrics
ex:additional-performance-metrics
hasMetricHas Metric(1)
- Vector Search Libraries
ex:vector-search-libraries
hasPerformanceMetricHas Performance Metric(1)
- Proof of Concept
ex:proof-of-concept
includesAccuracyMetricIncludes Accuracy Metric(1)
- Additional Performance Metrics
ex:additional-performance-metrics
includesMetricIncludes Metric(1)
- Vector Search Comparison
ex:vector-search-comparison
measuresMeasures(1)
- Proof of Concept
ex:proof-of-concept
orderedSuggestionOrdered Suggestion(1)
- Assistant
ex:assistant
relatesToRelates to(1)
- Task 3 Full Integration of Evaluation Metrics
ex:task-3-full-integration-of-evaluation-metrics
seeksImprovementSeeks Improvement(1)
- User
ex:user
storesValueStores Value(1)
- Results
ex:results
suggestedMetricSuggested Metric(1)
- Assistant
ex:assistant
usesMetricUses Metric(1)
- Vector Search Comparison Matrix
ex:vector-search-comparison-matrix
Other facts (20)
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.
20 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Performance Metric | [1] |
| Rdf:type | Performance Metric | [2] |
| Rdf:type | Performance Metric | [3] |
| Rdf:type | Metric | [4] |
| Rdf:type | Performance Metric | [5] |
| Rdf:type | Metric | [6] |
| Rdf:type | Metric | [7] |
| Rdf:type | Performance Metric | [8] |
| Description | The percentage of correct nearest neighbors retrieved. | [3] |
| Measures | Retrieval Accuracy | [5] |
| Contrasts With | Search Time | [5] |
| Formula | true_positives / (true_positives + false_negatives) | [6] |
| Component of | F1 Score | [6] |
| Inverse of | Has Recall Rate | [7] |
| Has Unit | ratio | [7] |
| Correlates With | Precision Rate | [7] |
| Higher Is Better | true | [7] |
| Has Current Measurement | 90 | [8] |
| Measurement Unit | percent | [8] |
| Has Current Value | 90 | [8] |
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.
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typebeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:PerformanceMetric
—
labelbeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
recall_rate
—
typebeam/7962136c-c338-4cc2-87ff-eaf945be2841
ex:PerformanceMetric
—
labelbeam/7962136c-c338-4cc2-87ff-eaf945be2841
recall_rate
—
typebeam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
ex:PerformanceMetric
—
labelbeam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
Recall Rate
—
descriptionbeam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01
The percentage of correct nearest neighbors retrieved.
—
typebeam/1ff666a3-024a-43b9-a61b-238256feb9fd
ex:Metric
—
labelbeam/1ff666a3-024a-43b9-a61b-238256feb9fd
Recall Rate
—
typebeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:PerformanceMetric
—
labelbeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
Recall Rate
—
measuresbeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:retrieval-accuracy
—
contrastsWithbeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:search-time
—
typebeam/6dbe8f35-74b9-40c2-9797-0debc6fb19f9
ex:Metric
—
formulabeam/6dbe8f35-74b9-40c2-9797-0debc6fb19f9
true_positives / (true_positives + false_negatives)
—
componentOfbeam/6dbe8f35-74b9-40c2-9797-0debc6fb19f9
ex:f1-score
—
typebeam/4839e02a-4d69-40e5-9fd1-d54a40659285
ex:Metric
—
labelbeam/4839e02a-4d69-40e5-9fd1-d54a40659285
recall_rate
—
inverseOfbeam/4839e02a-4d69-40e5-9fd1-d54a40659285
ex:hasRecallRate
—
hasUnitbeam/4839e02a-4d69-40e5-9fd1-d54a40659285
ratio
—
correlatesWithbeam/4839e02a-4d69-40e5-9fd1-d54a40659285
ex:precision-rate
—
higherIsBetterbeam/4839e02a-4d69-40e5-9fd1-d54a40659285
true
—
typebeam/cd20f999-1387-4a3e-9486-0da4fc043940
ex:PerformanceMetric
—
labelbeam/cd20f999-1387-4a3e-9486-0da4fc043940
Recall rate
—
hasCurrentMeasurementbeam/cd20f999-1387-4a3e-9486-0da4fc043940
90
—
measurementUnitbeam/cd20f999-1387-4a3e-9486-0da4fc043940
percent
—
hasCurrentValuebeam/cd20f999-1387-4a3e-9486-0da4fc043940
90
References (8)
8 references
ctx:claims/beam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3- full textbeam-chunktext/plain1 KB
doc:beam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3Show excerpt
8. **Ease of Integration**: How easy it is to integrate the database into your existing system. 9. **Community Support**: The level of community support and documentation available. 10. **Cost**: The financial cost associated with using the…
ctx:claims/beam/7962136c-c338-4cc2-87ff-eaf945be2841- full textbeam-chunktext/plain1 KB
doc:beam/7962136c-c338-4cc2-87ff-eaf945be2841Show excerpt
matrix.loc['Annoy 1.18.0', 'memory_usage'] = 600 matrix.loc['Hnswlib 0.9.2', 'memory_usage'] = 580 matrix.loc['Qdrant 0.8.1', 'memory_usage'] = 520 matrix.loc['Weaviate 1.14.0', 'memory_usage'] = 560 matrix.loc['Milvus 2.3.0', 'storage_siz…
ctx:claims/beam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01- full textbeam-chunktext/plain1 KB
doc:beam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01Show excerpt
matrix.loc['Faiss 1.7.3', 'search_time'] = 200 matrix.loc['Annoy 1.18.0', 'search_time'] = 250 matrix.loc['Hnswlib 0.9.2', 'search_time'] = 220 matrix.loc['Qdrant 0.8.1', 'search_time'] = 190 matrix.loc['Weaviate 1.14.0', 'search_time'] = 2…
ctx:claims/beam/1ff666a3-024a-43b9-a61b-238256feb9fd- full textbeam-chunktext/plain1 KB
doc:beam/1ff666a3-024a-43b9-a61b-238256feb9fdShow excerpt
matrix.loc['Weaviate 1.14.0', 'indexing_time'] = 360 matrix.loc['Milvus 2.3.0', 'memory_usage'] = 500 matrix.loc['Faiss 1.7.3', 'memory_usage'] = 550 matrix.loc['Annoy 1.18.0', 'memory_usage'] = 600 matrix.loc['Hnswlib 0.9.2', 'memory_usag…
ctx:claims/beam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53- full textbeam-chunktext/plain1 KB
doc:beam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53Show excerpt
matrix = pd.DataFrame(index=databases, columns=metrics) # Fill in the matrix with sample data matrix.loc['Milvus 2.3.0', 'search_time'] = 180 matrix.loc['Faiss 1.7.3', 'search_time'] = 200 matrix.loc['Annoy 1.18.0', 'search_time'] = 250 ma…
ctx:claims/beam/6dbe8f35-74b9-40c2-9797-0debc6fb19f9- full textbeam-chunktext/plain1 KB
doc:beam/6dbe8f35-74b9-40c2-9797-0debc6fb19f9Show excerpt
true_positives = sum([1 for vec in retrieved_neighbors if vec in true_neighbors]) false_positives = len(retrieved_neighbors) - true_positives false_negatives = len(true_neighbors) - true_positives recall_rate = true_positive…
ctx:claims/beam/4839e02a-4d69-40e5-9fd1-d54a40659285ctx:claims/beam/cd20f999-1387-4a3e-9486-0da4fc043940- full textbeam-chunktext/plain1 KB
doc:beam/cd20f999-1387-4a3e-9486-0da4fc043940Show excerpt
2. **Advanced Hyperparameter Tuning**: Allocate 3-4 hours. 3. **Full Integration of Evaluation Metrics**: Allocate 2-3 hours. 4. **Complete Integration with Existing Systems**: Allocate 3-4 hours. 5. **Comprehensive Error Handling and Loggi…
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