Accuracy, Recall, and Precision as Metrics for Analyzing Machine Learning Model Performance

What these metrics are, when to use them, and when not to use them

Accuracy, precision, and recall are three fundamental metrics used to evaluate the performance of classification models in machine learning. Each metric provides a different perspective on how well the model is performing, and understanding their differences is crucial for interpreting model results effectively. Here’s an overview of each metric, along with their pros and cons, and guidance on when they are most useful or not recommended.

Accuracy

Definition: Accuracy measures the overall correctness of the model, calculated as the ratio of correct predictions (both true positives and true negatives) to the total number of predictions made.

Pros:
- Simple and intuitive: It provides a quick snapshot of the model’s performance.
- Effective when the classes are balanced and the cost of false positives and false negatives is similar.

Cons:
- Can be misleading in imbalanced datasets where the majority class dominates. For example, in a dataset with 95% of class A and 5% of class B, a model that always predicts class A will have a high accuracy of 95%, despite not being able to identify class B at all.
- Does not account for the type of errors (false positives and false negatives).