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ML |使用 Sklearn 实现 KNN 分类器

原文:https://www.geeksforgeeks.org/ml-实现-KNN-分类器-使用-sklearn/

先决条件: K 近邻算法

k 近邻算法是机器学习中最基本也是最基本的分类算法之一。它属于监督学习领域,在模式识别、数据挖掘和入侵检测中有着广泛的应用。由于它是非参数的,也就是说,它不会对数据的分布做出任何潜在的假设(与 GMM 等其他算法相反,后者假设给定数据为高斯分布),因此在现实场景中可以广泛使用。

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

步骤 1: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

第二步: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

第三步: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]

第四步:评估模型

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

我们现在试图找到‘k’的最佳值,即最近邻居的数量。

第五步:绘制训练和考试成绩图

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。



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