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Which ML Metric Best Measures Generalization to Unseen Data in Real-World Applications?
Discover why cross-validation score is key for assessing machine learning model generalization to unseen data, outperforming accuracy, precision, and recall for reliable real-world deployment and overfitting prevention.
Question
When evaluating a machine learning model’s performance for real-world application, which metric helps assess how well the model generalizes to unseen data?
A. Cross-validation score
B. Recall
C. Accuracy
D. Precision
Answer
A. Cross-validation score
Explanation
Cross-validation score is the primary metric for assessing how well a machine learning model generalizes to unseen data during evaluation for real-world applications, as it systematically partitions the dataset into multiple train-test splits, trains the model on each subset, and averages performance across folds to provide a robust estimate of out-of-sample accuracy that minimizes overfitting risks and variance from single holdout sets. In contrast, recall measures true positive capture rates on a fixed test set without guaranteeing generalization; accuracy reflects overall correctness but falters with class imbalance; and precision evaluates positive prediction quality similarly limited to evaluated data, none inherently testing across varied unseen partitions like cross-validation.