如何在python中减少模型中的“噪声”？

import pandas as pd
from sklearn.preprocessing import StandardScaler

# 示例数据
data = pd.DataFrame({
    'feature1': [1, 2, 3, 4, 5],
    'feature2': [5, 4, 3, 2, 1]
})

# 处理缺失值
data.fillna(data.mean(), inplace=True)

# 标准化数据
scaler = StandardScaler()
data[['feature1', 'feature2']] = scaler.fit_transform(data[['feature1', 'feature2']])

from sklearn.feature_selection import SelectKBest, f_regression

# 示例数据
X = data[['feature1', 'feature2']]
y = [1, 2, 3, 4, 5]

# 选择最好的两个特征
selector = SelectKBest(score_func=f_regression, k=2)
X_new = selector.fit_transform(X, y)

from scipy.ndimage import gaussian_filter

# 示例图像数据
import numpy as np
image = np.random.rand(10, 10)

# 高斯滤波去噪
denoised_image = gaussian_filter(image, sigma=1)