Python 机器学习核心入门与实战进阶 Day 3 - 决策树 & 随机森林模型实战

✅ 今日目标

• 理解决策树（Decision Tree）的基本原理
• 掌握信息熵、基尼系数等分裂标准
• 使用 DecisionTreeClassifier 和 RandomForestClassifier 构建模型
• 学会可视化决策树与查看特征重要性
• 对比单棵树与集成模型（随机森林）的泛化能力

📘 一、决策树模型简介

🧠 二、常用模型 API

决策树：

from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier(max_depth=3, criterion='gini')
clf.fit(X_train, y_train)

随机森林：

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

📊 三、评估方式建议

📈 四、可视化与分析

from sklearn.tree import plot_tree
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plot_tree(clf, feature_names=["成绩", "性别"], class_names=["不及格", "及格"], filled=True)
plt.show()

# 特征重要性
import pandas as pd
importance = rf.feature_importances_
pd.DataFrame({"特征": ["成绩", "性别"], "重要性": importance})

💡 今日思路建议

1. 构建同样的“是否及格预测”分类数据集
2. 训练决策树模型，尝试调节 max_depth 查看影响
3. 训练随机森林模型，查看是否提升性能
4. 输出特征重要性对比
5. 可视化决策树结构图

📁 练习脚本：decision_tree_forest_demo.py

# 决策树 & 随机森林实战：预测学生是否及格

from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

plt.rcParams['font.family'] = 'Arial Unicode MS'  # Mac 用户可用
plt.rcParams['axes.unicode_minus'] = False
# 1. 构造数据
np.random.seed(42)
size = 100
scores = np.random.randint(40, 100, size)
genders = np.random.choice([0, 1], size=size)
labels = (scores >= 60).astype(int)

# 标准化成绩 + 性别作为特征
X = np.column_stack(((scores - scores.mean()) / scores.std(), genders))
y = labels

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 2. 决策树模型
dt_model = DecisionTreeClassifier(max_depth=3, criterion='gini', random_state=42)
dt_model.fit(X_train, y_train)
y_pred_dt = dt_model.predict(X_test)

print("=== 决策树模型评估 ===")
print("准确率：", accuracy_score(y_test, y_pred_dt))
print(classification_report(y_test, y_pred_dt))

# 决策树可视化
plt.figure(figsize=(10, 6))
plot_tree(dt_model, feature_names=["成绩", "性别"], class_names=["不及格", "及格"], filled=True)
plt.title("决策树可视化")
plt.tight_layout()
plt.show()

# 3. 随机森林模型
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
y_pred_rf = rf_model.predict(X_test)

print("\n=== 随机森林模型评估 ===")
print("准确率：", accuracy_score(y_test, y_pred_rf))
print(classification_report(y_test, y_pred_rf))

# 特征重要性对比
feature_importance = rf_model.feature_importances_
features = ["成绩", "性别"]
importance_df = pd.DataFrame({"特征": features, "重要性": feature_importance})
print("\n=== 特征重要性（随机森林） ===")
print(importance_df)

运行输出：

=== 决策树模型评估 ===
准确率： 1.0
              precision    recall  f1-score   support

           0       1.00      1.00      1.00         7
           1       1.00      1.00      1.00        13

    accuracy                           1.00        20
   macro avg       1.00      1.00      1.00        20
weighted avg       1.00      1.00      1.00        20