90% of Classification Models Use a 0.5 Probability Threshold

In the world of machine learning, classification models are often evaluated using a confusion matrix, which compares expected and predicted values to assess accuracy. This matrix is crucial for understanding model performance and identifying areas for improvement. Classification models, despite producing discrete outputs, inherently deal with uncertainty, often expressing results as probabilities of class membership. A common practice is setting a decision threshold at 0.5 to convert these probabilities into discrete classes. However, this threshold can be adjusted based on the specific needs of the application or the model’s ability to accurately reflect the data. By varying this threshold, analysts can fine-tune the model to achieve optimal performance for different scenarios.

Source: towardsdatascience.com