logit回归预测python代码

以下是一个简单的logistic回归模型预测的Python代码示例：

import pandas as pd
from sklearn.linear_model import LogisticRegression

# 导入数据
data = pd.read_csv("data.csv")

# 划分训练集和测试集
train = data.sample(frac=0.8, random_state=1)
test = data.drop(train.index)

# 准备特征和标签
X_train = train[['feature1', 'feature2', 'feature3']]
y_train = train['label']
X_test = test[['feature1', 'feature2', 'feature3']]
y_test = test['label']

# 创建模型并拟合训练集
clf = LogisticRegression(random_state=0).fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 打印评估指标
print("Accuracy:", clf.score(X_test, y_test))
print("Precision:", precision_score(y_test, y_pred))
print("Recall:", recall_score(y_test, y_pred))

请注意，此代码是基础示例，并且需要进行修改以适应特定的数据和任务。