Accuracy and F1 Score — The Better Choices for Evaluating Model Success

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The most logical metric is likely accuracy. Accuracy is helpful because it helps us to compute the amount of correct predictions a model makes because it includes true positives and true negatives. The following is the formula for accuracy: Accuracy = True Positives + True Negatives / Total Predictions. Accuracy helps us to address the following question — “What percentage of all of our model’s projections were correct?” The most commonly applied metric for classification tasks is accuracy because it gives us a clear overview of our model’s overall results.

F1 Score

The F1 score is more difficult to understand, but it is also more insightful because the harmonic mean of precision and recall is represented by the F1 score. Basically, the F1 score cannot be strong without also being strong in precision and recall. Whenever a model’s F1 score is high, you should be confident that it is performing well in all areas. The following is the formula for F1 score: F1 score = 2 * (Precision * Recall/Precision + Recall). Typically, if a model’s precision or recall is skewed too strongly, the F1 score penalizes it severely. As a result, the F1 score is the most commonly used metric for describing a model’s efficiency, no matter the machine learning task.

How to Evaluate the Evaluation Metrics

As I mentioned in my precision and recall post, the most critical metrics for a project are often determined by the business usage case or priorities for the model. That’s why it’s important to know why you’re doing your particular machine learning task and how the model results can be put into practice — or else the model could be optimized for the incorrect metric. It’s worth noting that, when in question, it’s a smart idea to measure all applicable metrics.



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