56 lines
1.3 KiB
Markdown
56 lines
1.3 KiB
Markdown
# K近邻法 (K-NN)
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<p align="center">
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<img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%207.jpg">
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</p>
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## 数据集 | 社交网络
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<p align="center">
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<img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Other%20Docs/data.PNG">
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</p>
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## 导入相关库
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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```
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## 导入数据集
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```python
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dataset = pd.read_csv('Social_Network_Ads.csv')
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X = dataset.iloc[:, [2, 3]].values
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y = dataset.iloc[:, 4].values
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```
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## 将数据划分成训练集和测试集
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```python
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
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```
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## 特征缩放
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```python
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from sklearn.preprocessing import StandardScaler
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sc = StandardScaler()
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X_train = sc.fit_transform(X_train)
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X_test = sc.transform(X_test)
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```
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## 使用K-NN对训练集数据进行训练
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```python
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from sklearn.neighbors import KNeighborsClassifier
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classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
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classifier.fit(X_train, y_train)
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```
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## 对测试集进行预测
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```python
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y_pred = classifier.predict(X_test)
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```
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## 生成混淆矩阵
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```python
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from sklearn.metrics import confusion_matrix
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cm = confusion_matrix(y_test, y_pred)
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```
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