python机器学习

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

#引入数据
digits=load_digits()
X=digits.data
y=digits.target

#train_size表示记录学习过程中的某一步,比如在10%,25%…的过程中记录一下
train_size,train_loss,test_loss=learning_curve(
SVC(gamma=0.1),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.figure()
#将每一步进行打印出来
plt.plot(train_size,train_loss_mean,‘o-’,color=‘m’,label=‘Training’)
plt.plot(train_size,test_loss_mean,‘o-’,color=‘cyan’,label=‘Cross-validation’)
plt.legend(‘best’)
plt.show()


机器学习之散点图
from sklearn import datasets#引入数据集
#构造的各种参数可以根据自己需要调整
X,y=datasets.make_regression(n_samples=100,n_features=1,n_targets=1,noise=1)

###绘制构造的数据###
import matplotlib.pyplot as plt
plt.figure()
plt.scatter(X,y)
plt.show()