Choosing a Classification Model Test Procedure | by Viyaleta Apgar | January, 2025
Is Recall / Precision better than Sensitivity / Specificity?

An easy way to test the validity of a classification model is to pair the expected values with the predicted values from the model and count all the cases where we were right or wrong; i.e. – creating a confusion matrix.
For anyone who has encountered classification problems in machine learning, the confusion matrix is a familiar concept. It plays an important role in helping us evaluate classification models and provides clues on how to improve their performance.
Although classification functions can produce different results, these models often have some degree of uncertainty.
Many model results can be expressed in terms of class membership probabilities. In general, decision limit which allows the model to map output probabilities to a different class set in the prediction step. Typically, this probability threshold is set to 0.5.
However, depending on the use case and how well the model can capture the relevant information, this limitation can be adjusted. We can analyze how the model works in various parameters to achieve the desired results.