上面代表单条特征，下面代表多条特征的损失函数
2.误差度量：
准确率：=TP+Tn/（TP+FP+FN+TN）
查准率/精确率=TP/(TP+FP) 举例：抓小偷，务必提高精确率，预测准确，让好人少进来
查全率/召回率=TP/(TP+FN)举例：FN是假阴性：新冠病人用的这个
灵敏度 TPR
roc 曲线
X:fpr
Y:TPR
围成面积：auc
1.导包2. 拟合数据3.预测4.度量
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix