Classification metrics can't handle a mix of binary and continuous targets
时间: 2024-06-01 16:12:15 浏览: 16
This statement is partially true. Classification metrics, such as accuracy, precision, recall, and F1 score, are designed to evaluate the performance of models that predict categorical targets, such as binary (0/1) or multi-class (e.g., A/B/C).
If the target variable is continuous, such as in regression problems, different metrics are used, such as mean squared error (MSE), mean absolute error (MAE), and R-squared.
However, in some cases, the target variable may have a mix of binary and continuous values, which requires a different approach. For example, in medical diagnosis, a model may predict the probability of a disease (continuous value) and then classify patients as having the disease or not based on a threshold (binary value). In such cases, hybrid metrics such as area under the receiver operating characteristic curve (AUC-ROC) and precision-recall curve (AUC-PR) can be used to evaluate the model's performance.
In summary, while classification metrics are not suitable for evaluating models that predict continuous targets, there are hybrid metrics that can handle a mix of binary and continuous targets.
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