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)...
average : string, [None, ‘micro’, ‘macro’(default), ‘samples’, ‘weighted’] >>>import numpy as np >>>from sklearn.metrics import roc_auc_score >>>y_true = np.array([0, 0, 1, 1]) >>>y_scores = np.array([0.1, 0.4, 0.35, 0.8]) >>>roc_auc_score(y_true, y_scor...
>>> from sklearn.metrics import fbeta_score, make_scorer >>> ftwo_scorer = make_scorer(fbeta_score, beta=2) >>> from sklearn.grid_search import GridSearchCV >>> from sklearn.svm import LinearSVC >>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=ftwo_s...
1.explained_variance_score(y_true,y_pred,sample_weight=None,multioutput='uniform_average'):回归方差(反应自变量与因变量之间的相关程度) explained_variance_score:解释方差分,这个指标用来衡量我们模型对数据集波动的解释程度,如果取值为1时,模型就完美,越小效果就越差 # 例子 from sklearn.metrics import exp...
>>> 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 >>> ...
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)...
简介:sklearn.metric.accuracy_score评价指标介绍和使用 1.示例 #!/usr/bin/python# -*- coding: UTF-8 -*-"""@author:livingbody@file:accuracy_score.py@time:2022/08/29"""from sklearn.metrics import accuracy_scoreif __name__ == '__main__':y_pred = [0, 2, 1, 3, 4]y_true = [...
result =accuracy_score(y_true, y_pred)return{"accuracy": result} 开发者ID:PavelOstyakov,项目名称:pipeline,代码行数:20,代码来源:accuracy.py 示例3: classification_scores ▲点赞 6▼ # 需要导入模块: from sklearn import metrics [as 别名]# 或者: from sklearn.metrics importaccuracy_score[as 别...
f1_score : 浮点数或者是浮点数数组,shape=[唯一标签的数量]二分类中的正类的F1 score或者是多分类任务中每个类别F1 score的加权平均. 下面来看下sklearn中计算F1的示例: from sklearn.metrics import f1_score y_true = [0, 1, 2, 0, 1, 2] y_pred = [0, 2, 1, 0, 0, 1] print(f1_score...
accuracy_score的参数 accuracy_score有两个参数,第一个是真实的标签(y_true),必须是一个1D数组,第二个是预测的标签(y_pred),也必须是一个1D数组,两者的长度必须相等。 例如: 。 ```python。 from sklearn.metrics import accuracy_score。 y_true = [0, 1, 0, 1, 1]。 y_pred = [0, 1, 1, ...