Classification metrics can't handle a mix of unknown and multiclass targets怎么解决
时间: 2024-06-03 10:10:00 浏览: 17
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 message typically occurs when you are trying to use a classification metric that is only applicable for multiclass targets (i.e. discrete labels) on a dataset that has both continuous and multiclass targets.
For example, if you have a dataset where some of the targets are categorical variables (e.g. "red", "blue", "green"), and some are continuous variables (e.g. the height of a person), you cannot use a multiclass classification metric like accuracy or F1 score to evaluate the performance of a machine learning model on this dataset.
Instead, you should use an appropriate evaluation metric that can handle both continuous and categorical variables, such as mean squared error (MSE) or mean absolute error (MAE) for the continuous variables and accuracy or AUC-ROC for the categorical variables. Alternatively, you could consider transforming the continuous variables into categorical variables, for example by binning them into discrete categories, so that you can use a multiclass classification metric.