随机森林分类模型构建流程

数据准备阶段需要处理结构化数据，通常包含用户特征（年龄、收入、浏览历史等）和二分类标签（购车意向为1/0）。使用pandas进行数据加载与预处理：

import pandas as pd
from sklearn.model_selection import train_test_split

data = pd.read_csv('user_behavior.csv')
X = data.drop('purchase_flag', axis=1)
y = data['purchase_flag']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

特征工程处理要点

分类变量需进行独热编码，数值变量建议标准化处理。使用ColumnTransformer构建自动化处理流水线：

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler

categorical_cols = ['gender', 'education']
numeric_cols = ['age', 'income']

preprocessor = ColumnTransformer(
    transformers=[
        ('cat', OneHotEncoder(), categorical_cols),
        ('num', StandardScaler(), numeric_cols)
    ])

模型训练与参数优化

使用GridSearchCV进行超参数调优，重点关注n_estimators和max_depth参数：

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_estimators': [100, 200, 300],
    'max_depth': [5, 10, None],
    'min_samples_split': [2, 5]
}

rf = RandomForestClassifier(random_state=42)
grid_search = GridSearchCV(rf, param_grid, cv=5)
grid_search.fit(preprocessor.fit_transform(X_train), y_train)

模型评估指标选择

分类任务需关注精确率、召回率和F1分数，特别是不平衡数据集：

from sklearn.metrics import classification_report

y_pred = grid_search.predict(preprocessor.transform(X_test))
print(classification_report(y_test, y_pred))

特征重要性分析

随机森林提供feature_importances_属性，可视化关键决策因素：

import matplotlib.pyplot as plt

feature_importances = grid_search.best_estimator_.feature_importances_
features = preprocessor.get_feature_names_out()
plt.barh(features, feature_importances)
plt.xlabel('Feature Importance')
plt.show()

模型部署建议

使用joblib保存训练好的管道模型，确保预处理和预测一体化：

from joblib import dump

best_model = grid_search.best_estimator_
dump({'model': best_model, 'preprocessor': preprocessor}, 'car_purchase_model.joblib')

实际应用中应考虑模型监控和定期重新训练机制，特别是用户行为模式可能随时间变化。对于线上预测服务，建议使用Flask或FastAPI构建API接口。