Datawhale动手数据分析第三章——模型搭建和评估

学习资料：https://github.com/datawhalechina/hands-on-data-analysis

第三章 模型搭建和评估

经过前面的两章知识点的学习，完成了对数据的基本了解，数据清洗，特征工程，数据可视化。这一章是使用数据，运用我们的数据以及结合业务来得到某些我们需要知道的结果。简单说就是：选择模型 → 输入数据 → 得到输出结果 → 评价模型 → 调整模型。

数据处理与载入库

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

data = pd.read_csv('clear_data.csv')

数据切分

X = data.drop('Survived', axis=1)
y = data['Survived']

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

参数说明：

• test_size：测试集占比，默认 0.25
• random_state：随机种子，保证结果可复现
• stratify：按比例分层抽样

模型搭建

逻辑回归

lr = LogisticRegression(max_iter=1000)
lr.fit(X_train, y_train)
y_pred_lr = lr.predict(X_test)

随机森林

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

模型评估

print("Logistic Regression Accuracy:", accuracy_score(y_test, y_pred_lr))
print("Random Forest Accuracy:", accuracy_score(y_test, y_pred_rf))
print("\nClassification Report (Random Forest):")
print(classification_report(y_test, y_pred_rf))

常用评估指标：

指标	说明
Accuracy	准确率：预测正确的样本占比
Precision	精确率：预测为正的样本中真正为正的比例
Recall	召回率：真正为正的样本中被正确预测的比例
F1-Score	精确率和召回率的调和平均数

模型调参

交叉验证

from sklearn.model_selection import cross_val_score

scores = cross_val_score(rf, X, y, cv=5)
print(f"5-fold CV accuracy: {scores.mean():.4f} (+/- {scores.std() * 2:.4f})")

网格搜索

from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20],
    'min_samples_split': [2, 5, 10]
}

grid_search = GridSearchCV(RandomForestClassifier(random_state=42),
                          param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)
print(f"Best params: {grid_search.best_params_}")
print(f"Best score: {grid_search.best_score_:.4f}")