Model Evaluation is the process of assessing the performance, accuracy, and effectiveness of machine learning models using various metrics and techniques to determine how well they generalize to new, unseen data.

Common evaluation methods include:

• Cross-Validation: Splitting data into training and testing sets multiple times
• Holdout Validation: Using separate test datasets not seen during training
• Performance Metrics: Accuracy, precision, recall, F1-score, ROC-AUC, etc.
• Confusion Matrix: Visual representation of classification performance
• Learning Curves: Plotting performance vs. training data size

Evaluation helps identify overfitting, underfitting, and bias issues while guiding model selection and hyperparameter tuning. Different metrics are used based on the problem type (classification, regression, clustering) and business objectives.