Accuracy alone is often misleading. Choosing the right metric for your problem is critical.

Classification Metrics

• Accuracy: % correct — misleading with imbalanced classes
• Precision: Of predicted positives, how many are actually positive?
• Recall: Of actual positives, how many did we find?
• F1 Score: Harmonic mean of precision and recall
• AUC-ROC: Overall classifier performance across thresholds

Regression Metrics

• MAE: Mean Absolute Error — average prediction error
• RMSE: Root Mean Squared Error — penalizes large errors more
• R²: Proportion of variance explained by the model

Reference:

ML classification metrics

https://developers.google.com/machine-learning/crash-course/classification/accuracy-precision-recall