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AUC

AUC (Area Under the Curve) is a metric for binary classification problems, representing the area under the ROC (Receiver Operating Characteristic)

Key Concepts

Represents the area under the ROC curve.

AUC values range from 0 to 1, where 1 indicates perfect classification and 0.5 suggests no discriminative power (equivalent to random guessing).

Roc and Auc Score

The roc_auc_score is a function from the sklearn.metrics module in Python that computes the Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores. It is a widely used metric for evaluating the performance of binary classification models.

Key Points about roc_auc_score:

  • Purpose: It quantifies the overall ability of the model to discriminate between the positive and negative classes across all possible classification thresholds.
  • Range: The score ranges from 0 to 1, where:
    • 1 indicates perfect discrimination (the model perfectly distinguishes between the positive and negative classes).
    • 0.5 suggests no discriminative power (equivalent to random guessing).
    • Values below 0.5 indicate a model that performs worse than random guessing.
  • Input: The function takes the true binary labels and the predicted probabilities (or decision function scores) as inputs.
  • Output: It returns a single scalar value representing the AUC.

Example Code

from sklearn.metrics import roc_auc_score
 
# Actual and predicted values
y_act = [1, 0, 1, 1, 0]
y_pred = [1, 1, 0, 1, 0]
 
# Compute AUC
auc = roc_auc_score(y_act, y_pred)
print(f'AUC: {auc}')

Graph View