构建LogisticRegression模型，完成训练 计算常见指标，并绘制PR曲线和ROC曲线 调整模型参数（penalty, C, solver），观察指标变化情况

首先导入需要的包，读取数据集：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc, precision_recall_curve

# 读取数据集
data = pd.read_csv('data.csv')
X = data.iloc[:, :-1]
y = data.iloc[:, -1]

接着，将数据集分为训练集和测试集，使用默认参数构建LogisticRegression模型，训练模型并进行预测：

# 分割数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 构建模型
model = LogisticRegression()

# 训练模型
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

计算常见指标：

# 计算指标
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

print('Accuracy: {:.2f}'.format(accuracy))
print('Precision: {:.2f}'.format(precision))
print('Recall: {:.2f}'.format(recall))
print('F1-score: {:.2f}'.format(f1))

绘制PR曲线和ROC曲线：

# 绘制PR曲线
precision, recall, _ = precision_recall_curve(y_test, y_pred)
plt.plot(recall, precision)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('PR Curve')
plt.show()

# 绘制ROC曲线
fpr, tpr, _ = roc_curve(y_test, y_pred)
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, label='ROC curve (area = {:.2f})'.format(roc_auc))
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc='lower right')
plt.show()

调整模型参数，观察指标变化情况：

# 调整模型参数
for penalty in ['l1', 'l2']:
    for C in [0.001, 0.01, 0.1, 1, 10, 100]:
        for solver in ['liblinear', 'saga']:
            # 构建模型
            model = LogisticRegression(penalty=penalty, C=C, solver=solver)

            # 训练模型
            model.fit(X_train, y_train)

            # 预测
            y_pred = model.predict(X_test)

            # 计算指标
            accuracy = accuracy_score(y_test, y_pred)
            precision = precision_score(y_test, y_pred)
            recall = recall_score(y_test, y_pred)
            f1 = f1_score(y_test, y_pred)

            print('Penalty: {}, C: {}, Solver: {}'.format(penalty, C, solver))
            print('Accuracy: {:.2f}'.format(accuracy))
            print('Precision: {:.2f}'.format(precision))
            print('Recall: {:.2f}'.format(recall))
            print('F1-score: {:.2f}'.format(f1))
            print('-' * 50)

通过调整模型参数，可以找到最优的参数组合，从而得到最好的模型。