PR曲线实则是以precision(精准率)和recall(召回率)这两个为变量而做出的曲线,其中recall为横坐标,precision为纵坐标。设定一系列阈值,计算每个阈值对应的recall和precision,即可计算出PR曲线各个点。 precision=tp / (tp + fp) recall=tp / (tp + fn) 可以用sklearn.metrics.precision_recall_curve计算PR曲线 fro...
例子: >>>importnumpyasnp>>>fromsklearn.metricsimportprecision_recall_fscore_support>>>y_true = np.array(['cat','dog','pig','cat','dog','pig'])>>>y_pred = np.array(['cat','pig','dog','cat','cat','dog'])>>>precision_recall_fscore_support(y_true, y_pred, average='macr...
display:PrecisionRecallDisplay 例子: >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import PrecisionRecallDisplay >>> from sklearn.model_selection import train_test_split >>> from sklearn.linear_model import LogisticRegression >...
from sklearnimportmetrics metrics.precision_score(y_true,y_pred,average='micro')# 微平均,精确率 Out[130]:0.33333333333333331metrics.precision_score(y_true,y_pred,average='macro')# 宏平均,精确率 Out[131]:0.375metrics.precision_score(y_true,y_pred,labels=[0,1,2,3],average='macro')# 指定...
python中想要计算如上指标主要是使用sklearn包中预先写好的函数。可以使用以下代码进行计算: fromsklearn.metricsimportprecision_score, recall_score, f1_score, accuracy_scorey_true = [...]# 正确的标签y_pred = [...]# 预测的标签# 计算正确率accuracy = accuracy_score(y_true, y_pred)# 计算精确度...
使用python画precision-recall曲线的代码是: sklearn.metrics.precision_recall_curve(y_true, probas_pred, pos_label=None, sample_weight=None) 以上代码会根据预测值和真实值,并通过改变判定阈值来计算一条precision-recall典线。 注意:以上命令只限制于二分类任务 precision(精度)为tp / (tp + fp),其中tp为...
from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression def logisticregression(): '''逻辑回归癌症预测''' # 确定数据columns数值 columns = ["Sample code number","Clump Thickness","Uniformity of Cell Size...
from sklearn.metrics import precision_score, recall_score from sklearn.datasets import load_breast_cancer # 加载乳腺癌数据集 data = load_breast_cancer() X, y = data.data, data.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, ...
基于混淆矩阵,我们可以计算出几个关键的性能指标,如精确率(Precision)、召回率(Recall)和F1分数(F1 Score)。混淆矩阵(Confusion Matrix)是一种特别有用的工具,用于评估分类模型的性能,它展示了实际值与模型预测值之间的关系。 示例代码: importnumpyasnpfromsklearn.datasetsimportmake_classificationfromsklearn.model_...
准确率(precision): 准确率是精确性的度量,表示正确预测的正样本数占所有预测为正样本的数量的比值,也就是说所有预测为正样本的样本中有多少是真正的正样本。注意,precision只关注预测为正样本的部分,而accuracy考虑全部样本。 召回率(recall): 又称为查全率,是覆盖面的度量,表示正确预测的正样本数占真实正样本总数...