文章作者：Tyan 博客：noahsnail.com | | 简书
本文主要介绍scikit-learn中的交叉验证。通过交叉验证来选取KNN算法中的K值。
Demo 1 import numpy as np from sklearn import datasets from sklearn.cross_validation import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.cross_validation import cross_val_score # 加载iris数据集 iris = datasets.load_iris() # 读取特征 X = iris.data # 读取分类标签 y = iris.target # 定义分类器 knn = KNeighborsClassifier(n_neighbors = 5) # 进行交叉验证数据评估, 数据分为5部分, 每次用一部分作为测试集 scores = cross_val_score(knn, X, y, cv = 5, scoring = 'accuracy') # 输出5次交叉验证的准确率 print scores 结果 [ 0.96666667 1. 0.93333333 0.96666667 1. ] Demo 2 import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.cross_validation import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.cross_validation import cross_val_score # 确定knn中k的取值 # 加载iris数据集 iris = datasets.load_iris() # 读取特征 X = iris.data # 读取分类标签 y = iris.target # 定义knn中k的取值, 0-10 k_range = range(1, 30) # 保存k对应的准确率 k_scores = [] # 计算每个k取值对应的准确率 for k in k_range: # 获得knn分类器 knn = KNeighborsClassifier(n_neighbors = k) # 对数据进行交叉验证求准确率 scores = cross_val_score(knn, X, y, cv = 10, scoring = 'accuracy') # 保存交叉验证结果的准确率均值 k_scores.append(scores.mean()) # 绘制k取不同值时的准确率变化图像 plt.plot(k_range, k_scores) plt.xlabel('K Value in KNN') plt.ylabel('Cross-Validation Mean Accuracy') plt.show() 结果