2026.1.15总结

了解机器学习基础内容和scikit-learn

2. 机器学习基础
# scikit-learn 入门
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score# 加载数据
iris = load_iris()
X, y = iris.data, iris.target# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 训练模型
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)# 评估
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"模型准确率: {accuracy:.2%}")