Evaluating model performance and accuracy

After building a churn prediction system, it's crucial to assess how well it works. This evaluation helps determine if the system is reliable and where it might need improvement.

Key aspects of evaluating churn prediction performance include:

• Accuracy metrics: Measure how often the system correctly predicts churn
• Confusion matrix: Shows true positives, false positives, true negatives, and false negatives
• ROC curve: Illustrates the trade-off between correctly identifying churners and non-churners
• Cross-validation: Tests the system on different subsets of data to ensure consistent performance

For example, a subscription service might find their system correctly identifies 80% of churners but also incorrectly flags 20% of loyal customers as potential churners.

Understanding these metrics helps in refining the prediction system and setting realistic expectations.^[1]