特征选择算法在实际项目中的效果评估

在大模型训练中，特征选择是数据工程的关键环节。本文将通过实际案例展示几种主流特征选择算法的效果。

实验环境

import numpy as np
import pandas as pd
from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

数据准备与预处理

# 模拟数据集（实际项目中应使用真实数据）
np.random.seed(42)
n_samples, n_features = 1000, 20
X = np.random.randn(n_samples, n_features)
y = (X[:, 0] + X[:, 1] - X[:, 2] > 0).astype(int)

# 添加噪声特征
for i in range(15, 20):
    X[:, i] = np.random.randn(n_samples)

# 划分训练测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

特征选择算法对比

# 1. 基于统计的特征选择（F检验）
f_selector = SelectKBest(score_func=f_classif, k=10)
X_train_f = f_selector.fit_transform(X_train, y_train)
X_test_f = f_selector.transform(X_test)

# 2. 基于互信息的特征选择
mi_selector = SelectKBest(score_func=mutual_info_classif, k=10)
X_train_mi = mi_selector.fit_transform(X_train, y_train)
X_test_mi = mi_selector.transform(X_test)

# 3. 基于随机森林的特征重要性
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)
feature_importance = rf.feature_importances_

效果评估

# 评估不同方法的效果
models = {
    'F-Test': (f_selector, X_train_f, X_test_f),
    'Mutual Info': (mi_selector, X_train_mi, X_test_mi)
}

for name, (selector, X_tr, X_te) in models.items():
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_tr, y_train)
    y_pred = model.predict(X_te)
    accuracy = accuracy_score(y_test, y_pred)
    print(f'{name} Accuracy: {accuracy:.4f}')

通过实验可以发现，基于互信息的方法通常能获得更好的特征选择效果，特别适合处理非线性关系。在实际项目中，建议结合业务场景和数据特点选择合适的特征选择策略。