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...
accuracy_score(y_true, y_pred[, normalize, …]) classification_report(y_true, y_pred[, …]) f1_score(y_true, y_pred[, labels, …]) fbeta_score(y_true, y_pred, beta[, labels, …]) hamming_loss(y_true, y_pred[, classes]) jaccard_similarity_score(y_true, y_pred[, …]) ...
1.1. accuracy_score() 计算所有样本中分类正确样本所占的比例 语法 ## 语法sklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None) y_true:y的真实值 y_pred:y的预测值 normalize:若为True(默认),返回分类得分,若为False,返回分类正确的样本个数 sample_weight:样本权重 ...
>>> 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 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,以及模型的泛化能力。
accuracy_score 精确性,模型评估指标之一 详细参数 class sklearn.linear_model.LogisticRegression (penalty=’l2’, dual=False, tol=0.0001, C=1.0,fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=’warn’, max_iter=100,multi_class=’warn’, verbose=0, warm_...
accuracy_score(y_true, y_pred) #0.5 输出结果 accuracy_score(y_true, y_pred, normalize=False) #2 输出结果 2 recall_score :召回率=提取出的正确信息条数/样本中的信息条数。通俗地说,就是所有准确的条目有多少被检索出来了。 klearn.metrics.recall_score(y_true, y_pred, labels=None, pos_label...
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* ...
第一种方式:accuracy_score 代码语言:javascript 复制 # 准确率importnumpyasnp from sklearn.metricsimportaccuracy_score y_pred=[0,2,1,3,9,9,8,5,8]y_true=[0,1,2,3,2,6,3,5,9]accuracy_score(y_true,y_pred)Out[127]:0.33333333333333331accuracy_score(y_true,y_pred,normalize=False)# 类似...