Compute hyperml/balanced_accuracy via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of hyperml/balanced_accuracy.
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---
name: hyperml-balanced-accuracy
description: Compute hyperml/balanced_accuracy via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of hyperml/balanced_accuracy.
metadata:
skill_kind: metric
source_lib: huggingface-evaluate
hf_module: hyperml/balanced_accuracy
source: library_introspection
---
# hyperml-balanced-accuracy
> Metric `hyperml/balanced_accuracy` from the HuggingFace `evaluate` library.
## When to invoke
User asks to compute `hyperml/balanced_accuracy` or wants HF evaluate's canonical version.
## Recipe
```python
import evaluate
metric = evaluate.load("hyperml/balanced_accuracy")
result = metric.compute(predictions=preds, references=refs)
print(result)
```
## Don'ts
- Don't assume your in-house `hyperml/balanced_accuracy` matches HF — version conventions vary.
- Many evaluate metrics have task-specific arguments (`average=`, `lang=`, `model_type=`); read the metric card before reporting numbers.
