Default Regression Metrics

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

Regression Metrics

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

Metric	Internal Name	Abbreviation
Explained Variance Score	explained_variance_score
Max Error	max_error
Mean Absolute Error	mean_absolute_error	MAE
Mean Absolute Percentage Error	mean_absolute_percentage_error	MAPE
Mean Pinball Loss	mean_pinball_loss
Mean Squared Error	mean_squared_error	MSE
Mean Squared Log Error	mean_squared_log_error
Median Absolute Error	median_absolute_error
R2 Score	r2_score	R2
Root Mean Squared Error	root_mean_squared_error	RMSE
Root Mean Squared Log Error	root_mean_squared_log_error
Concordance Correlation Coefficient	concordance_correlation_coefficient	CCC
Negative Mean Absolute Error	neg_mean_absolute_error	NegMAE
Adjusted R2 Score	adjusted_r2_score	AdjR2

Usage

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

from brisk import REGRESSION_METRICS

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

# Or select specific metrics
METRIC_CONFIG = brisk.MetricManager(
    REGRESSION_METRICS[0],  # explained_variance_score
    REGRESSION_METRICS[2],  # mean_absolute_error
)