target = [3, 4, 4, 3] # 真实的值 f1 = f1_score(pred, target , labels = labels , pos_label= 3) # pos_label指定正样本的值是多少 print(f1 ) ===> 0.5 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 2、sklearn.metrics.f1_score() 的使用举例(多分类) 针对多分类问题,各个参数...
from sklearn.metrics import precision_score print(precision_score(labels,predictions)*100) 1. 2. F1得分 F1得分取决于召回和精确度,它是这两个值的调和平均值。 我们考虑调和平均值除以算术平均值,因为想要低召回率或精确度来产生低F1分数。在之前的例子中,召回率为100%,精确度为20%,算术平均值为60%,而...
from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import f1_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_...
#导入相应的函数库fromsklearn.metricsimportaccuracy_scorefromsklearn.metricsimportprecision_scorefromsklearn.metricsimportconfusion_matrixfromsklearn.metricsimportclassification_reportfromsklearn.metricsimportcohen_kappa_scorefromsklearn.metricsimportf1_scorefromsklearn.ensembleimportRandomForestClassifierfromsklearnimpo...
一、acc、recall、F1、混淆矩阵、分类综合报告 1、准确率 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]:0.33333333333333...
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...
python sklearn计算准确率、精确率、召回率、F1 score https://blog.csdn.net/hfutdog/article/details/88085878 混淆矩阵 准确率 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 ...
from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 加载数据集 iris = load_iris() X = iris.data y = iris.target # 因为逻辑回归是用于二分类问题,我们这里只取两个类别的数据 # 选择类别为0和1的数据 ...
f1_score(precision, recall) # Out[6]: # 0.0 from sklearn import datasets digits = datasets.load_digits() X = digits.data y = digits.target.copy() # 手动使数据变得偏斜 y[digits.target==9] = 1 y[digits.target!=9] = 0 ...
sklearn第三方库可以帮助我们快速完成任务,使用方法如下: fromsklearn.metricsimportconfusion_matrix confusion_matrix(y_true,y_pred)pred=multilayer_perceptron(x,weights,biases)correct_prediction=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))wit...