scikit-learn的基本用法(六)——交叉验证2

文章作者：Tyan
博客：noahsnail.com | CSDN | 简书

本文主要介绍scikit-learn中的交叉验证。

• Demo

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


# 加载数据集
digits = load_digits()
X = digits.data
y = digits.target
# 用SVM进行学习并记录loss
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])

# 训练误差均值
train_loss_mean = -np.mean(train_loss, axis = 1)
# 测试误差均值
test_loss_mean = -np.mean(test_loss, axis = 1)

# 绘制误差曲线
plt.plot(train_sizes, train_loss_mean, 'o-', color = 'r', label = 'Training')
plt.plot(train_sizes, test_loss_mean, 'o-', color = 'g', label = 'Cross-Validation')

plt.xlabel('Training data size')
plt.ylabel('Loss')
plt.legend(loc = 'best')
plt.show()

• 结果

![image](文章作者：Tyan
博客：noahsnail.com | CSDN | 简书

本文主要介绍scikit-learn中的交叉验证。

• Demo

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


# 加载数据集
digits = load_digits()
X = digits.data
y = digits.target
# 用SVM进行学习并记录loss
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])

# 训练误差均值
train_loss_mean = -np.mean(train_loss, axis = 1)
# 测试误差均值
test_loss_mean = -np.mean(test_loss, axis = 1)

# 绘制误差曲线
plt.plot(train_sizes, train_loss_mean, 'o-', color = 'r', label = 'Training')
plt.plot(train_sizes, test_loss_mean, 'o-', color = 'g', label = 'Cross-Validation')

plt.xlabel('Training data size')
plt.ylabel('Loss')
plt.legend(loc = 'best')
plt.show()

• 结果

![image](文章作者：Tyan
博客：noahsnail.com  |  CSDN  |  简书

本文主要介绍scikit-learn中的交叉验证。

• Demo

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


# 加载数据集
digits = load_digits()
X = digits.data
y = digits.target
# 用SVM进行学习并记录loss
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])

# 训练误差均值
train_loss_mean = -np.mean(train_loss, axis = 1)
# 测试误差均值
test_loss_mean = -np.mean(test_loss, axis = 1)

# 绘制误差曲线
plt.plot(train_sizes, train_loss_mean, 'o-', color = 'r', label = 'Training')
plt.plot(train_sizes, test_loss_mean, 'o-', color = 'g', label = 'Cross-Validation')

plt.xlabel('Training data size')
plt.ylabel('Loss')
plt.legend(loc = 'best')
plt.show()

• 结果

image

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