如何在实际代码中显示这种图形？

在Python中，你可以使用`sklearn.model_selection.learning_curve`函数结合`matplotlib`库来创建这类图表。以下是一个简单的例子：

import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression

# 创建模拟数据
X, y = make_regression(n_samples=1000, n_features=10, noise=0.1)

# 定义模型
model = LinearRegression()

# 获取训练集和测试集的变化数据
train_sizes, train_scores, test_scores = learning_curve(model, X, y, train_sizes=np.linspace(0.1, 1.0, 10), 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)
test_scores_mean = np.mean(test_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)

# 绘制图形
plt.plot(train_sizes, train_scores_mean, label='Training Error')
plt.plot(train_sizes, test_scores_mean, label='Testing Error')
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.title('Learning Curves')
plt.xlabel("Number of Training Samples")
plt.ylabel("Mean Squared Error")
plt.legend()
plt.show()

这段代码会生成一个图像，横轴表示训练样本数，纵轴是误差，阴影区域反映了误差的标准差。你可以根据需要替换模型、数据生成和评分函数。