In the ML development lifecycle, evaluating model performance using various metrics is crucial. Here’s a breakdown of key metrics for classification and regression models, and how business metrics align with ML goals.
Classification Metrics
- False Positive Rate (FPR):
Measures how often the model incorrectly predicts a positive class.
Formula:FPR = FP / (FP + TN) - True Negative Rate (TNR):
Measures how often the model correctly predicts the negative class.
Formula:TNR = TN / (FP + TN) - AUC (Area Under the Curve):
Evaluates binary classification by plotting True Positive Rate vs False Positive Rate. - AUC score ranges from 0 to 1. A score of 1 indicates perfect accuracy, and 0.5 means random predictions.
Regression Metrics
- Mean Squared Error (MSE):
Measures the average of squared differences between predicted and actual values. - Smaller MSE indicates better model performance.
- Root Mean Squared Error (RMSE):
The square root of MSE, making it easier to interpret as it matches the unit of the dependent variable. - Mean Absolute Error (MAE):
Averages the absolute errors and is less sensitive to outliers than MSE and RMSE.
Business Metrics
- Business Impact:
ML models should be evaluated based on how they solve business problems, such as increasing sales, improving customer feedback, or reducing costs. - Cost of Errors:
Consider the costs incurred from model errors (e.g., lost customers or sales). - ROI (Return on Investment):
Calculate ROI by comparing actual results with business goals and assessing the cost of building and operating the model. - Cost Allocation in AWS:
Use AWS cost allocation tags to track charges for ML projects and ensure they align with the business goals.
