Python中怎样进行逻辑回归？

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

# 加载数据集
iris = load_iris()
X = iris.data[:, :2]  # 我们只使用前两个特征
y = (iris.target != 0) * 1  # 转换为二分类问题

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 初始化逻辑回归模型
model = LogisticRegression()

# 训练模型
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 打印准确率
print(f'模型准确率: {model.score(X_test, y_test):.2f}')

import numpy as np

class MyLogisticRegression:
    def __init__(self, learning_rate=0.01, num_iterations=1000):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        self.weights = None
        self.bias = None

    def sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0

        for _ in range(self.num_iterations):
            linear_model = np.dot(X, self.weights) + self.bias
            y_predicted = self.sigmoid(linear_model)

            dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))
            db = (1 / n_samples) * np.sum(y_predicted - y)

            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db

    def predict(self, X):
        linear_model = np.dot(X, self.weights) + self.bias
        y_predicted = self.sigmoid(linear_model)
        return (y_predicted >= 0.5).astype(int)

# 使用自定义逻辑回归模型
model = MyLogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = np.mean(y_pred == y_test)
print(f'自定义模型准确率: {accuracy:.2f}')