Model Evaluation and Responsible Use

Focuses on assessing model performance, identifying overfitting and underfitting, and considering fairness, bias, and ethical deployment concerns.

Compute and interpret common evaluation metrics such as accuracy, precision, recall, F1-score, and mean squared error
Detect overfitting and underfitting by comparing training and test performance
Use validation strategies to improve confidence in model generalization
Identify sources of bias in datasets and model predictions
Explain key ethical and practical considerations related to fairness, transparency, privacy, and responsible machine learning deployment

Configuration Management and Documentation