from sklearn.model_selection import learning_curve
from sklearn.datasets import load_digits
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np

digits=load_digits()
X=digits.data
y=digits.target
train_sizes,train_loss,test_loss=learning_curve(
    SVC(gamma=0.001),X,y,cv=10,scoring='neg_mean_squared_error',
    train_sizes=[0.1,0.25,0.5,0.75,1]
)
train_loss_mean=-np.mean(train_loss,axis=1)
test_loss_mean=-np.mean(test_loss,axis=1)
plt.plot(train_sizes,train_loss_mean,'o-',color='r',label='Training')
plt.plot(train_sizes,test_loss_mean,'o-',color='b',label='Cross-Validation')
plt.xlabel("Training Examples")
plt.ylabel("Loss")
plt.legend(loc="best")
plt.show()