Default Classification Metrics

Brisk provides a set of predefined classification metrics wrapped as MetricWrapper instances. These metrics are sourced from scikit-learn and are ready to use in your projects without additional configuration.

Classification Metrics

Once imported you can select these metrics using internal name or abbreviation.

Metric	Internal Name	Abbreviation
Accuracy	accuracy
Precision	precision
Recall	recall
F1 Score	f1_score	f1
Balanced Accuracy	balanced_accuracy	bal_acc
Top-k Accuracy Score	top_k_accuracy	top_k
Log Loss	log_loss
Area Under the ROC Curve	roc_auc
Brier Score Loss	brier
Receiver Operating Characteristic	roc

Usage

To use these metrics in your Brisk project, you can import them directly:

from brisk import CLASSIFICATION_METRICS

# In your metrics.py file
METRIC_CONFIG = brisk.MetricManager(
    *CLASSIFICATION_METRICS
)

# Or select specific metrics
METRIC_CONFIG = brisk.MetricManager(
    CLASSIFICATION_METRICS[0],  # accuracy
    CLASSIFICATION_METRICS[2],  # recall
)