recall_score
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
recall_score has 24 facts recorded in Dontopedia across 5 references, with 6 live disagreements.
24 facts·13 predicates·5 sources·6 in dispute
Mostly:rdf:type(4), takes argument(3), provided by(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
callsCalls(1)
- Calculate Metrics Function
ex:calculate-metrics-function
computedByComputed by(1)
- Recall
ex:recall
containsFunctionContains Function(1)
- Sklearn Metrics
ex:sklearn-metrics
providesProvides(1)
- Sklearn Metrics
ex:sklearn-metrics
usesFunctionUses Function(1)
- Recall Calculation
ex:recall-calculation
Other facts (22)
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.
22 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Sklearn Metric Function | [2] |
| Rdf:type | Evaluation Function | [3] |
| Rdf:type | Metric Function | [4] |
| Rdf:type | Scikit Learn Function | [5] |
| Takes Argument | Test Df Labels | [3] |
| Takes Argument | Predictions | [3] |
| Takes Argument | Zero Division Parameter | [5] |
| Provided by | Sklearn Metrics | [1] |
| Provided by | Scikit Learn | [5] |
| Compares | Test Df Labels | [3] |
| Compares | Predictions | [3] |
| Has Argument | zero_division=1 | [4] |
| Has Argument | Zero Division Argument | [5] |
| Takes Arguments | Y True Parameter | [5] |
| Takes Arguments | Y Pred Parameter | [5] |
| Returns | Recall | [3] |
| Evaluation Metric | true | [3] |
| Sets Zero Division | 1 | [4] |
| Is Machine Learning Metric | true | [4] |
| Handles Zero Division | 1 | [4] |
| Called by | Calculate Metrics Function | [5] |
| Handles | Undefined Recall | [5] |
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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providedBybeam/93caa9c5-4b7e-4e32-b8aa-eab422d02ac5
ex:sklearn-metrics
—
typebeam/166e449f-f01f-4d52-b7b4-50e375d9caff
ex:SklearnMetricFunction
—
typebeam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106
ex:EvaluationFunction
—
labelbeam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106
recall_score
—
takesArgumentbeam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106
ex:test-df-labels
—
takesArgumentbeam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106
ex:predictions
—
returnsbeam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106
ex:recall
—
comparesbeam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106
ex:test-df-labels
—
comparesbeam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106
ex:predictions
—
evaluationMetricbeam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106
true
—
typebeam/2b7229d1-a1ff-4ee9-bc85-d3c33a30acd6
ex:MetricFunction
—
hasArgumentbeam/2b7229d1-a1ff-4ee9-bc85-d3c33a30acd6
zero_division=1
—
setsZeroDivisionbeam/2b7229d1-a1ff-4ee9-bc85-d3c33a30acd6
1
—
isMachineLearningMetricbeam/2b7229d1-a1ff-4ee9-bc85-d3c33a30acd6
true
—
handlesZeroDivisionbeam/2b7229d1-a1ff-4ee9-bc85-d3c33a30acd6
1
—
typebeam/e439b65d-d477-4a00-b619-b77ab784c2c2
ex:ScikitLearnFunction
—
labelbeam/e439b65d-d477-4a00-b619-b77ab784c2c2
recall_score
—
calledBybeam/e439b65d-d477-4a00-b619-b77ab784c2c2
ex:calculate-metrics-function
—
hasArgumentbeam/e439b65d-d477-4a00-b619-b77ab784c2c2
ex:zero-division-argument
—
handlesbeam/e439b65d-d477-4a00-b619-b77ab784c2c2
ex:undefined-recall
—
providedBybeam/e439b65d-d477-4a00-b619-b77ab784c2c2
ex:Scikit-learn
—
takesArgumentsbeam/e439b65d-d477-4a00-b619-b77ab784c2c2
ex:y-true-parameter
—
takesArgumentsbeam/e439b65d-d477-4a00-b619-b77ab784c2c2
ex:y-pred-parameter
—
takesArgumentbeam/e439b65d-d477-4a00-b619-b77ab784c2c2
ex:zero_division-parameter
References (5)
5 references
ctx:claims/beam/93caa9c5-4b7e-4e32-b8aa-eab422d02ac5- full textbeam-chunktext/plain1 KB
doc:beam/93caa9c5-4b7e-4e32-b8aa-eab422d02ac5Show excerpt
[Turn 393] Assistant: Evaluating the accuracy of document parsing tools like Apache Tika and PDFBox involves comparing the extracted text against a ground truth. To measure accuracy, you can use metrics such as precision, recall, and F1-sco…
ctx:claims/beam/166e449f-f01f-4d52-b7b4-50e375d9caff- full textbeam-chunktext/plain1 KB
doc:beam/166e449f-f01f-4d52-b7b4-50e375d9caffShow excerpt
print(f"Precision: {precision}, Recall: {recall}, F1 Score: {f1_score}") ``` Can you help me fill in the evaluation logic and suggest some additional metrics I can use? ->-> 1,1 [Turn 6081] Assistant: Certainly! Evaluating the performance …
ctx:claims/beam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106- full textbeam-chunktext/plain1 KB
doc:beam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106Show excerpt
# Train the model model = SparseModel() model.fit(train_df) # Make predictions predictions = model.predict(test_df) # Calculate the recall score recall = recall_score(test_df['label'], predictions) print(f'Recall score: {recall:.3f}') ```…
ctx:claims/beam/2b7229d1-a1ff-4ee9-bc85-d3c33a30acd6- full textbeam-chunktext/plain1 KB
doc:beam/2b7229d1-a1ff-4ee9-bc85-d3c33a30acd6Show excerpt
By following these steps, you can ensure that your evaluation pipeline is robust, transparent, and continuously improving. [Turn 9436] User: hmm, can I integrate these logging improvements into my existing CI/CD pipeline? [Turn 9437] Assi…
ctx:claims/beam/e439b65d-d477-4a00-b619-b77ab784c2c2- full textbeam-chunktext/plain1 KB
doc:beam/e439b65d-d477-4a00-b619-b77ab784c2c2Show excerpt
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') def calculate_metrics(y_true, y_pred): accuracy = accuracy_score(y_true, y_pred) precision = precision_score(y_true, y_pred, zero_division=…
See also
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