准确性得分ValueError:无法处理二进制目标和连续目标的混合 [英] Accuracy Score ValueError: Can't Handle mix of binary and continuous target

问题描述

我正在使用scikit-learn中的linear_model.LinearRegression作为预测模型.它有效且完美.我在使用accuracy_score指标评估预测结果时遇到问题.

I'm using linear_model.LinearRegression from scikit-learn as a predictive model. It works and it's perfect. I have a problem to evaluate the predicted results using the accuracy_score metric.

这是我的真实数据:

array([1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0])

我的预测数据:

array([ 0.07094605,  0.1994941 ,  0.19270157,  0.13379635,  0.04654469,
    0.09212494,  0.19952108,  0.12884365,  0.15685076, -0.01274453,
    0.32167554,  0.32167554, -0.10023553,  0.09819648, -0.06755516,
    0.25390082,  0.17248324])

我的代码:

accuracy_score(y_true, y_pred, normalize=False)

错误消息:

ValueError:无法处理二进制目标和连续目标的混合情况

ValueError: Can't handle mix of binary and continuous target

帮助吗?谢谢.

推荐答案

编辑(后注释):以下内容将解决编码问题，但强烈建议不要使用此方法，因为线性回归模型的分类效果很差，很可能无法正确地分类.

EDIT (after comment): the below will solve the coding issue, but is highly not recommended to use this approach because a linear regression model is a very poor classifier, which will very likely not separate the classes correctly.

阅读下面@desertnaut精心编写的答案，解释为什么此错误暗示着机器学习方法中的某些错误，而不是您必须修复"的某些错误.

Read the well written answer below by @desertnaut, explaining why this error is an hint of something wrong in the machine learning approach rather than something you have to 'fix'.

accuracy_score(y_true, y_pred.round(), normalize=False)

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