README.md

KNN 算法

KNN基础学习详见--> https://github.com/LiuChuang0059/Machine_Learning/blob/master/Statical_Learning/Chapter_3-KNN/README.md

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KNN 算法工作简介

• 主要： 对未标记的对象进行标记
• 计算实例点与标记的对象之间的距离，确定其k近邻点
• 使用周边数量最多的类标签来确定该对象的标签

KNN算法实现

1 数据

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2 算法实现

Importing the libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Importing the dataset

dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

Splitting the dataset into the Training set and Test set

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

Feature Scaling

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

Fitting K-NN to the Training set

from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)

Predicting the Test set results

y_pred = classifier.predict(X_test)

visualisation-------详见--->Lpgistic_regression

plot_decision_regions(X_test, y_pred, classifier=cl)
plt.title("Test set")

结果

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附

完整代码

数据集

问题

• 尝试自实现knn分类算法

• 改变调整参数---效果

参考

KNeighborsClassfier参数： http://sklearn.apachecn.org/cn/0.19.0/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier

KNeighborsClassfier使用 ： http://sklearn.apachecn.org/cn/0.19.0/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py

KNN 算法python实现 ： https://www.cnblogs.com/ybjourney/p/4702562.html