Classification metrics can't handle a mix of unknown and multiclass targets怎么解决

该问题通常发生在使用分类指标（例如准确率、精确度、召回率等）时，预测目标包含未知类别。解决该问题的方法是将未知类别视为一个额外的类别，即将其变为多分类问题。可以使用One-vs-Rest（OvR）或One-vs-One（OvO）等多分类方法，将未知类别作为一个单独的类别来处理，并将其与其他所有类别区分开来。同时，也可以尝试使用适用于多分类和未知类别的特殊分类指标（例如Confusion Matrix、F1-Score等）。

相关问题

ValueError: Classification metrics can t handle a mix of continuous and multiclass targets

这个错误通常是由于你的模型输出的标签格式不正确导致的。分类指标无法处理连续值和多类别值的混合目标。你需要确保你的模型输出的标签是多类别的，而不是连续值。你可以尝试将标签进行 one-hot 编码，或者使用适当的损失函数来确保你的模型输出多类别值。如果你需要更具体的帮助，请提供更多的信息，我会尽力回答。

Classification metrics can't handle a mix of continuous and multiclass targets

This error occurs when you try to use classification metrics, such as accuracy or F1 score, on a target variable that has a mix of continuous and categorical values. Classification metrics are designed to work with categorical targets, where each observation belongs to one of a fixed set of categories.

If your target variable has continuous values, you should use regression metrics instead, such as mean squared error or R-squared. If your target variable has both continuous and categorical values, you may need to perform separate analyses on the different subsets of the data.

To avoid this error, make sure that your target variable is properly formatted as either a categorical or continuous variable, depending on the type of analysis you are performing.