compute_metrics
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
compute_metrics has 30 facts recorded in Dontopedia across 3 references, with 7 live disagreements.
Mostly:returns(4), has parameter(4), calls(4)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (7)
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.
calledByCalled by(2)
- Accuracy Score
ex:accuracy_score - F1 Score
ex:f1_score
usedInUsed in(2)
- Accuracy Score
ex:accuracy-score - F1 Score
ex:f1-score
followedByFollowed by(1)
- Train and Evaluate Function
ex:train-and-evaluate-function
hasFunctionHas Function(1)
- Python Script
ex:python-script
isComputedByIs Computed by(1)
- Accuracy Metric
ex:accuracy-metric
Other facts (28)
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 |
|---|---|---|
| Returns | accuracy | [1] |
| Returns | f1 | [1] |
| Returns | accuracy | [2] |
| Returns | f1 | [2] |
| Has Parameter | y_true | [2] |
| Has Parameter | y_pred | [2] |
| Has Parameter | y_true | [3] |
| Has Parameter | y_pred | [3] |
| Calls | accuracy_score | [2] |
| Calls | f1_score | [2] |
| Calls | Accuracy Score | [3] |
| Calls | F1 Score | [3] |
| Uses | accuracy_score | [2] |
| Uses | f1_score | [2] |
| Uses | y_true | [2] |
| Uses | y_pred | [2] |
| Rdf:type | Function | [1] |
| Rdf:type | Function | [2] |
| Rdf:type | Python Function | [3] |
| Parameter | y_true | [1] |
| Parameter | y_pred | [1] |
| Requires | y_true | [3] |
| Requires | y_pred | [3] |
| Has Name | compute_metrics | [2] |
| Returns Tuple | accuracy,f1 | [2] |
| Computes | Accuracy Metric | [3] |
| Calculates | Accuracy | [3] |
| Is Incomplete | true | [3] |
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 (3)
ctx:claims/beam/8c98e67e-181b-4bd3-959b-a984a9e85208- full textbeam-chunktext/plain1 KB
doc:beam/8c98e67e-181b-4bd3-959b-a984a9e85208Show excerpt
Collect or generate the data you will use to evaluate your metrics. This could be labeled data for classification tasks or any other relevant data for your specific use case. ### Step 3: Implement Automated Testing Use Scikit-learn to trai…
ctx:claims/beam/2bf979a4-4d10-40b9-9692-8653827a61e1- full textbeam-chunktext/plain1 KB
doc:beam/2bf979a4-4d10-40b9-9692-8653827a61e1Show excerpt
### Step 4: Modify Your Script for Logging Ensure your Python script logs the metrics to a file named `metrics.log`. Here's an updated version of the script: ```python import numpy as np from sklearn.datasets import make_classification fr…
ctx:claims/beam/42084a70-f90e-4de3-9339-1a01e0afa60e
See also
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