当normalize为True时,最好的表现是score为1,当normalize为False时,最好的表现是score未样本数量. #示例 import numpy as np from sklearn.metrics import accuracy_score y_pred = [0, 2, 1, 3] y_true = [0, 1, 2, 3] print(accuracy_score(y_true, y_pred)) # 0.5 print(accuracy_score(y_tr...
,可能是因为accuracy_score函数的参数类型不匹配或者数据预处理不正确。下面是一些可能导致错误的原因和解决方法: 1. 参数类型不匹配:accuracy_score函数的参数应该是预测结果...
sklearn.metrics.accuracy_score(y_true, y_pred,normalize=True,sample_weight=None) normalize:默认值为True,返回正确分类的比例;如果为False,返回正确分类的样本数 >>>importnumpyasnp >>>fromsklearn.metricsimportaccuracy_score >>>y_pred=[0,2,1,3] >>>y_true=[0,1,2,3] >>>accuracy_score(y_...
>>> from sklearn.metrics import accuracy_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> accuracy_score(y_true, y_pred) 0.5 >>> accuracy_score(y_true, y_pred, normalize=False) 2 在具有二进制标签指示符的多标签情况下: >>> import numpy as np >>> ...
accuracy_score sklearn accuracy_score 是 scikit-learn(sklearn)库中一个重要的评估指标,用于衡量模型预测结果与实际结果之间的误差。在机器学习中,预测准确率是一个非常重要的性能指标,而 accuracy_score 指标能够提供关于模型性能的量化描述。通过分析 accuracy_score,我们可以了解模型在训练数据上的表现,以及模型的...
from sklearn.metrics import accuracy_score yp = [1, 0, 1, 1] y = [1, 0, 0, 1] print("【显示】yp =",yp) print("【显示】y =",y) print("【执行】accuracy_score(yp, y)") print(accuracy_score(yp, y)) print("【执行】accuracy_score(y, yp)") ...
sklearn.metrics.accuracy_score(y_true,y_pred,*,normalize=True,sample_weight=None) 可用来计算分类准确率分数。 可用来计算多分类准确率分数。 """Accuracy classification score.In multilabel classification, this function computes subset accuracy:the set of labels predicted for a sample must *exactly* ...
from sklearn.metrics import accuracy_score yp = [1, 0, 1, 1]y = [1, 0, 0, 1]print("【显示】yp =",yp)print("【显示】y =",y)print("【执行】accuracy_score(yp, y)")print(accuracy_score(yp, y))print("【执行】accuracy_score(y, yp)")print(accuracy_score(y, yp)...
sklearn.metrics accuracy_score - Python accuracy_score是sklearn.metrics的一个函数,它可以用来计算分类模型的预测准确率。该函数将预测结果和真实结果进行比较,计算预测准确的比例。以下是使用accuracy_score的示例: from sklearn.metrics import accuracy_score y_true = [0, 1, 2, 3, 4] y_pred = [0, ...
L1范数 L1范数作为正则化项，会让模型参数θ稀疏话，就是让模型参数向量里为0的元素尽量多。L1就是...