模型过拟合问题

import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
from sklearn.ensemble import RandomForestRegressor
import numpy as np

# 假设我们有X和y作为数据和标签
model = RandomForestRegressor()

# 生成学习曲线
train_sizes, train_scores, test_scores = learning_curve(model, X, y, cv=5, scoring='neg_mean_squared_error')

# 计算平均值和标准差
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)

# 绘制学习曲线
plt.figure()
plt.title("Learning Curve")
plt.xlabel("Training examples")
plt.ylabel("Score")
plt.grid()

plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score")

plt.legend(loc="best")
plt.show()

from sklearn.linear_model import Ridge

# 使用L2正则化训练模型
model = Ridge(alpha=1.0)
model.fit(X_train, y_train)

from keras.preprocessing.image import ImageDataGenerator

# 创建数据增强生成器
datagen = ImageDataGenerator(rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest')

# 拟合数据
datagen.fit(X_train)