我正在运行逻辑回归,但我获得的 f1 分数为 0.0。我认为这与零除错误有关,但我无法修复它
data4=data[['Age','BusinessTravel_Travel_Frequently','DistanceFromHome','Education','EnvironmentSatisfaction','Gender_Male','JobInvolvement','YearsWithCurrManager','MaritalStatus_Married','JobSatisfaction','NumCompaniesWorked','TotalWorkingYears','TrainingTimesLastYear','YearsAtCompany','Performance_dummy']]
X1=data4[['Age','BusinessTravel_Travel_Frequently','DistanceFromHome','Education','EnvironmentSatisfaction','Gender_Male','JobInvolvement','YearsWithCurrManager','MaritalStatus_Married','JobSatisfaction','NumCompaniesWorked','TotalWorkingYears','TrainingTimesLastYear','YearsAtCompany']]
y1=data4.Performance_dummy
# split X and y into training and testing sets
from sklearn.model_selection import train_test_split
X_train1,X_test1,y_train1,y_test1=train_test_split(X1,y1,test_size=0.5,random_state=0,stratify=y1)
# import the class
from sklearn.linear_model import LogisticRegression
# instantiate the model (using the default parameters)
logreg1 = LogisticRegression(max_iter=1000)
# fit the model with data
logreg1.fit(X_train1,y_train1)
#
y_pred1=logreg1.predict(X_test1)
print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg1.score(X_test1, y_test1)))
我得到以下输出
Accuracy of logistic regression classifier on test set: 0.85
我运行了混淆矩阵代码,如下所示
from sklearn.metrics import confusion_matrix
confusion_matrix = confusion_matrix(y_test1, y_pred1)
print("Confusion Matrix:\n",confusion_matrix)
from sklearn.metrics import classification_report
print("Classification Report:\n",classification_report(y_test1, y_pred1,zero_division=1))
上述代码的输出
Confusion Matrix:
[[622 0]
[113 0]]
Classification Report:
precision recall f1-score support
0 0.85 1.00 0.92 622
1 1.00 0.00 0.00 113
accuracy 0.85 735
macro avg 0.92 0.50 0.46 735
weighted avg 0.87 0.85 0.78 735
我还运行了这段代码来了解测试数据中的结果比率,并得到以下输出,但我不确定如何修复这个零除错误
from collections import Counter
print(Counter(y_train1))
print(Counter(y_test1))
输出
Counter({0: 622, 1: 113})
Counter({0: 622, 1: 113})
您的
f1-score
定义不明确,因为您的模型仅预测一类 (0
)。
您可以在
class_weight="balanced"
上使用 LogisticRegression
来惩罚代表性不足的样本。
如果这不起作用,增加训练集大小或使用更高级的模型可能是明智之举。