毫升 |使用 Sklearn 实现 KNN 分类器

先决条件：K-最近邻算法

K-Nearest Neighbors 是机器学习中最基本但必不可少的分类算法之一。它属于监督学习领域，在模式识别、数据挖掘和入侵检测中得到广泛应用。它在现实生活场景中被广泛使用，因为它是非参数的，也就是说，它不对数据的分布做出任何潜在的假设（与 GMM 等其他算法相反，它假设给定数据的高斯分布） .

本文将演示如何使用Python的 Sklearn 库实现K-最近邻分类器算法。

第 1 步：导入所需的库

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt 
import seaborn as sns

第 2 步：读取数据集

cd C:\Users\Dev\Desktop\Kaggle\Breast_Cancer
# Changing the read file location to the location of the file
  
df = pd.read_csv('data.csv')
  
y = df['diagnosis']
X = df.drop('diagnosis', axis = 1)
X = X.drop('Unnamed: 32', axis = 1)
X = X.drop('id', axis = 1)
# Separating the dependent and independent variable
  
X_train, X_test, y_train, y_test = train_test_split(
             X, y, test_size = 0.3, random_state = 0)
# Splitting the data into training and testing data

第 3 步：训练模型

K = []
training = []
test = []
scores = {}
  
for k in range(2, 21):
    clf = KNeighborsClassifier(n_neighbors = k)
    clf.fit(X_train, y_train)
  
    training_score = clf.score(X_train, y_train)
    test_score = clf.score(X_test, y_test)
    K.append(k)
  
    training.append(training_score)
    test.append(test_score)
    scores[k] = [training_score, test_score]

第 4 步：评估模型

for keys, values in scores.items():
    print(keys, ':', values)

我们现在尝试找到“k”的最佳值，即最近邻居的数量。

第 5 步：绘制训练和测试分数图

ax = sns.stripplot(K, training);
ax.set(xlabel ='values of k', ylabel ='Training Score')  
  
plt.show()
# function to show plot

ax = sns.stripplot(K, test);
ax.set(xlabel ='values of k', ylabel ='Test Score')
plt.show()

plt.scatter(K, training, color ='k')
plt.scatter(K, test, color ='g')
plt.show()
# For overlapping scatter plots

从上面的散点图中，我们可以得出 k 的最佳值将在 5 左右的结论。