Evaluation
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Evaluation has 16 facts recorded in Dontopedia across 1 reference, with 3 live disagreements.
Mostly:uses metric(3), includes metric(3), rdf:type(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.
containsStepContains Step(1)
- Explanation Section
ex:explanation-section
demonstratesDemonstrates(1)
- Code Block
ex:code-block
enablesEnables(1)
- Explanation Step 5
ex:explanation-step-5
hasStepHas Step(1)
- Machine Learning Workflow
ex:machine-learning-workflow
precedesPrecedes(1)
- Explanation Step 5
ex:explanation-step-5
Other facts (15)
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 |
|---|---|---|
| Uses Metric | Recall Score | [1] |
| Uses Metric | Classification Report | [1] |
| Uses Metric | Confusion Matrix | [1] |
| Includes Metric | Recall Score | [1] |
| Includes Metric | Classification Report | [1] |
| Includes Metric | Confusion Matrix | [1] |
| Rdf:type | Explanation Step | [1] |
| Rdf:type | Evaluation Step | [1] |
| Step Number | 6 | [1] |
| Describes Relation | Code Block | [1] |
| Describes Purpose | model performance assessment | [1] |
| Uses Concept | model evaluation | [1] |
| Validates | Explanation Step 5 | [1] |
| Uses Output | Trained Model | [1] |
| Performs | Model Evaluation | [1] |
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 (1)
ctx:claims/beam/4b350633-6322-4093-993a-e7268aabef00- full textbeam-chunktext/plain1 KB
doc:beam/4b350633-6322-4093-993a-e7268aabef00Show excerpt
# Train the model model.fit(X_train_tfidf, y_train) # Make predictions predictions = model.predict(X_test_tfidf) # Calculate the recall score recall = recall_score(y_test, predictions) print(f'Recall score: {recall:.3f}') # Print classif…
See also
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