Classification metrics can't handle a mix of binary and continuous targets啥意思

这句话的意思是分类模型的评估指标不能同时处理二元（binary）和连续（continuous）的目标变量（targets）。分类模型常用的指标如准确率，召回率和 F1 分数等，这些指标是基于分类结果的，不能用于评估连续型目标变量的预测结果。同样地，回归模型的评估指标比如均方误差（MSE）和平均绝对误差（MAE）也无法处理分类型目标变量的预测结果。因此，在评估模型性能时，需要选择与目标变量类型相匹配的评估指标。

相关问题

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

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.

Classification metrics can't handle a mix of binary and continuous targets什么意思

这个错误提示通常会在使用分类模型进行回归分析时出现。在回归问题中，目标变量是连续的，而在二元分类问题中，目标变量是离散的（如0或1）。因此，当你尝试使用分类模型处理既包含连续型目标变量又包含二元目标变量的数据时，就会出现此错误。

这个错误提示的意思是分类模型无法同时处理连续型和二元型的目标变量，因为这两种类型的目标变量具有不同的度量标准和处理方式。因此，在处理这种类型的数据时，需要使用回归模型来处理连续型目标变量，使用分类模型来处理二元型目标变量，或者将数据进行转换，使其仅包含一种类型的目标变量。