11 ML Evaluation Metrics: A Comprehensive Overview, Comparisons, and Trade-off Analysis

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Let’s go through the interpretability, context consideration, and trade-off analysis for each of the evaluation metrics commonly used in machine learning:

1. Accuracy:

i. Interpretability:

• Easily understandable
• Represents the proportion of correctly classified instances (both true positives and true negatives)

ii. Context Consideration:

• Suitable for balanced class distributions
• May be misleading in imbalanced datasets. In scenarios with imbalanced classes, other metrics like precision, recall, F1 score, or area under the ROC curve (ROC-AUC) might provide a more nuanced evaluation

iii. Trade-off Analysis:

• Balanced accuracy may be more appropriate for imbalanced datasets.
• Accuracy does not distinguish between the types of errors i.e. it does not provide insights into the balance between false positives and false negatives. In imbalanced datasets, it may not accurately represent the model’s performance.
• It might not be the best metric when the cost of different errors varies.