Classification metrics can't handle a mix of binary and continuous targets 如何处理
时间: 2024-05-22 18:09:56 浏览: 12
If you have a mix of binary and continuous targets in a classification problem, you need to convert the continuous targets into binary targets before using binary classification metrics such as accuracy, precision, recall, or F1-score.
One approach is to use a threshold value to convert the continuous targets into binary targets. For example, if the threshold value is set to be 0.5, any value above 0.5 is considered as 1, and any value below 0.5 is considered as 0. This is a common technique used in logistic regression models to convert predicted probabilities into binary predictions.
Alternatively, you can use regression metrics such as mean squared error (MSE) or mean absolute error (MAE) to evaluate the performance of the model on the continuous targets. However, keep in mind that this approach does not take into account the binary nature of the target variable.
In summary, the approach you choose depends on the specific requirements of your problem and the metric you want to use to evaluate the performance of your model.
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