AI Terms Dictionary

Definition

This field involves analyzing metrics such as accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). It helps determine how well a model distinguishes between positive and negative classes, particularly when class distributions are imbalanced. Proper evaluation is critical for deploying reliable predictive systems in high-stakes environments like medical diagnosis or fraud detection.

Summary

The process of assessing the performance of machine learning models that predict one of two possible outcomes.

Key Concepts

• Confusion Matrix
• Precision-Recall Tradeoff
• ROC Curve
• F1-Score

Use Cases

• Medical disease screening
• Spam email filtering
• Credit risk assessment

Code Example

1
2

from sklearn.metrics import classification_report
print(classification_report(y_true, y_pred))

Related Terms

• confusion_matrix
• roc_auc
• precision_recall
• cross_validation