sklearn k最邻近算法

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1、介绍

k最邻近算法可以说是一个非常经典而且原理十分容易理解的算法，可以应用于分类和聚合。

优点 ：

         1、简单，易于理解，易于实现，无需估计参数，无需训练；

         2、适合对稀有事件进行分类；

         3、特别适合于多分类问题(multi-modal,对象具有多个类别标签)， kNN比SVM的表现要好；

缺点：

         1、对规模超大的数据集拟合时间较长，对高维数据拟合欠佳，对稀疏数据集束手无策

         2、当样本不平衡时，如一个类的样本容量很大，而其他类样本容量很小时，有可能导致当输入一个新样本时，该类样本并不接近目标样本

2、代码实际应用

• 分类

from sklearn.datasets import make_blobs
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as npX, y = make_blobs(n_samples=500, centers=5, random_state=8)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y_train))])clf = KNeighborsClassifier()
clf.fit(X_train, y_train)X_min, X_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1
y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1xx, yy = np.meshgrid(np.arange(X_min, X_max, .02), np.arange(y_min, y_max, .02))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)plt.pcolormesh(xx, yy, Z, cmap=plt.cm.spring)plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cmap)
# for idx, cl in enumerate(np.unique(y_train)):
#     plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cmap)# plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cmap, edgecolors='y', marker=markers[idx], alpha=0.8, linewidths=1)print("模型的正确率：{:.2f}".format(clf.score(X_test, y_test)) )
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("Classifier:KNN")
plt.show()

• 回归

from sklearn.datasets import make_regression
from sklearn.neighbors import KNeighborsRegressorimport matplotlib.pyplot as plt
import numpy as np# 生成随机回归数据
X, y = make_regression(n_features=1, n_informative=1, noise=50, random_state=8)reg = KNeighborsRegressor(n_neighbors=2)
reg.fit(X, y)
# 随机产生x的预测值，根据训练模型来预测y的值
z = np.linspace(-3, 3, 200).reshape(-1, 1)
plt.scatter(X, y, c='orange', edgecolors='k')plt.plot(z, reg.predict(z), c='k', linewidth=3)
print("模型评分：{:.2f}".format(reg.score(X, y)))
plt.title('KNN Regressor')
plt.show()

• 分类实际应用：将酒分类

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as npwine_dataset = load_wine()print(wine_dataset['data'].shape)
X_train, X_test, y_train, y_test = train_test_split(wine_dataset['data'], wine_dataset['target'], random_state=0)print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)clf = KNeighborsClassifier(n_neighbors=7)
clf.fit(X_train, y_train)print("测试数据集得分: {:.2f}".format(clf.score(X_test, y_test)))X_new = np.array([[13.2, 2.77, 2.51, 18.5, 96.6, 1.04, 2.55, 0.57, 1.47, 6.2, 1.05, 3.33, 820]])prediction = clf.predict(X_new)
print("预测新红酒的分类为：{}".format(wine_dataset['target_names'][prediction]))

 

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