Avoid Misleading in Machine Learning Classification Evaluation
Classification is a machine learning task that involves predicting the category (class) to which a given example belongs. For instance, in a credit card fraud detection system, the model determines whether a transaction is fraudulent or legitimate. To make this decision, the system uses a classification threshold—a predefined probability value. If a transaction's predicted probability exceeds this threshold, it is classified as fraudulent; otherwise, it is classified as legitimate.
In machine learning, confusion matrix is wisely used for classification task. It is represented as a table that shows how well a classification model performs by comparing predicted values to actual values.
Different thresholds usually result in different numbers of true/false positives and true/false negatives. The threshold should be adjusted based on the business purpose of the model because some mistakes are more costly than others. If false positives are much more costly or risky than false negatives, it makes sense to minimize them.
Along with the confusion matrix, Accuracy, Recall, Precision, and F1 Score are commonly used to evaluate model performance in machine learning classification tasks. These metric values vary as the classification threshold changes. Users often adjust the threshold to optimize a specific metric based on their priorities.
In classification tasks, evaluating model performance requires more than just accuracy. Metrics like Precision, Recall, F1 Score provide deeper insights of model performance. Since changing the classification threshold impacts these metrics, selecting the right threshold is crucial. Businesses must adjust the threshold based on their specific goals—whether minimizing false positives to reduce unnecessary alerts or prioritizing recall to catch more fraud cases. Ultimately, optimizing the threshold ensures the model aligns with real-world needs and balances performance trade-offs effectively.