python机器学习入门

以下是Python机器学习入门的步骤：

1. 导入所需的库和模块

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

2. 准备数据集

# 假设我们有一个数据集X和对应的标签y
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
y = np.array([0, 0, 1, 1])

3. 数据预处理

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 特征缩放
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

4. 构建模型

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

# 在训练集上训练模型
model.fit(X_train, y_train)

5. 模型评估

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)