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  • Python3入门人工智能 掌握机器学习 深度学习 提升实战能力5：机器学习其他常用技术

    • 决策树

     

     

     

     

     

     

     

     

     

     

     

     

    •   异常检测

     

     

     

     

     

     

     

     

     

     

     

    • 主成分分析

     

     

     

     

     

     

     

     

     

     

     

     

     

    • 实战准备

     

     

     

     

     

     

     

     

    • 实战一

     

     

     

     数据文件：iris_data.csv 内容为:

     

     

    1 #establish the decision tree model 创建决策树模型
2 from sklearn import tree
3 dc_tree = tree.DecisionTreeClassifier(criterion='entropy',min_samples_leaf=5)#建立分类器  参数1：调用默认信息增益最大化的方法 参数2：建立决策树的分类叶子数最少样本数
4 dc_tree.fit(x,y)

     

    1 #visualize the tree 图形展示
2 %matplotlib inline
3 from matplotlib import pyplot as plt
4 fig = plt.figure(figsize=(20,20)) #图形尺寸
5 tree.plot_tree(dc_tree,filled='True',feature_names=['SepalLength','SepalWidth','PetalLength','PetalWidth'],class_names=['setosa','versicolor','virginica'])#展示树状图 参数1：数据 参数2：背景填充色 参数3：属性名称 参数4：分类名称
     1 [Text(496.0, 978.48, 'PetalWidth <= 0.8
entropy = 1.585
samples = 150
value = [50, 50, 50]
class = setosa'),
 2  Text(372.0, 761.0400000000001, 'entropy = 0.0
samples = 50
value = [50, 0, 0]
class = setosa'),
 3  Text(620.0, 761.0400000000001, 'PetalWidth <= 1.75
entropy = 1.0
samples = 100
value = [0, 50, 50]
class = versicolor'),
 4  Text(372.0, 543.6, 'PetalLength <= 4.95
entropy = 0.445
samples = 54
value = [0, 49, 5]
class = versicolor'),
 5  Text(248.0, 326.1600000000001, 'SepalLength <= 5.15
entropy = 0.146
samples = 48
value = [0, 47, 1]
class = versicolor'),
 6  Text(124.0, 108.72000000000003, 'entropy = 0.722
samples = 5
value = [0, 4, 1]
class = versicolor'),
 7  Text(372.0, 108.72000000000003, 'entropy = 0.0
samples = 43
value = [0, 43, 0]
class = versicolor'),
 8  Text(496.0, 326.1600000000001, 'entropy = 0.918
samples = 6
value = [0, 2, 4]
class = virginica'),
 9  Text(868.0, 543.6, 'PetalLength <= 4.95
entropy = 0.151
samples = 46
value = [0, 1, 45]
class = virginica'),
10  Text(744.0, 326.1600000000001, 'entropy = 0.65
samples = 6
value = [0, 1, 5]
class = virginica'),
11  Text(992.0, 326.1600000000001, 'entropy = 0.0
samples = 40
value = [0, 0, 40]
class = virginica')]

    1 dc_tree = tree.DecisionTreeClassifier(criterion='entropy',min_samples_leaf=1)#建立分类器  参数1：调用默认信息增益最大化的方法 参数2：建立决策树的分类叶子数最少样本数
2 dc_tree.fit(x,y)
3 fig = plt.figure(figsize=(20,20)) #图形尺寸
4 tree.plot_tree(dc_tree,filled='True',feature_names=['SepalLength','SepalWidth','PetalLength','PetalWidth'],class_names=['setosa','versicolor','virginica'])#展示树状图 参数1：数据 参数2：背景填充色 参数3：属性名称 参数4：分类名称
     1 [Text(558.0, 996.6, 'PetalWidth <= 0.8
entropy = 1.585
samples = 150
value = [50, 50, 50]
class = setosa'),
 2  Text(472.15384615384613, 815.4000000000001, 'entropy = 0.0
samples = 50
value = [50, 0, 0]
class = setosa'),
 3  Text(643.8461538461538, 815.4000000000001, 'PetalWidth <= 1.75
entropy = 1.0
samples = 100
value = [0, 50, 50]
class = versicolor'),
 4  Text(343.38461538461536, 634.2, 'PetalLength <= 4.95
entropy = 0.445
samples = 54
value = [0, 49, 5]
class = versicolor'),
 5  Text(171.69230769230768, 453.0, 'PetalWidth <= 1.65
entropy = 0.146
samples = 48
value = [0, 47, 1]
class = versicolor'),
 6  Text(85.84615384615384, 271.79999999999995, 'entropy = 0.0
samples = 47
value = [0, 47, 0]
class = versicolor'),
 7  Text(257.53846153846155, 271.79999999999995, 'entropy = 0.0
samples = 1
value = [0, 0, 1]
class = virginica'),
 8  Text(515.0769230769231, 453.0, 'PetalWidth <= 1.55
entropy = 0.918
samples = 6
value = [0, 2, 4]
class = virginica'),
 9  Text(429.23076923076917, 271.79999999999995, 'entropy = 0.0
samples = 3
value = [0, 0, 3]
class = virginica'),
10  Text(600.9230769230769, 271.79999999999995, 'SepalLength <= 6.95
entropy = 0.918
samples = 3
value = [0, 2, 1]
class = versicolor'),
11  Text(515.0769230769231, 90.59999999999991, 'entropy = 0.0
samples = 2
value = [0, 2, 0]
class = versicolor'),
12  Text(686.7692307692307, 90.59999999999991, 'entropy = 0.0
samples = 1
value = [0, 0, 1]
class = virginica'),
13  Text(944.3076923076923, 634.2, 'PetalLength <= 4.85
entropy = 0.151
samples = 46
value = [0, 1, 45]
class = virginica'),
14  Text(858.4615384615383, 453.0, 'SepalLength <= 5.95
entropy = 0.918
samples = 3
value = [0, 1, 2]
class = virginica'),
15  Text(772.6153846153845, 271.79999999999995, 'entropy = 0.0
samples = 1
value = [0, 1, 0]
class = versicolor'),
16  Text(944.3076923076923, 271.79999999999995, 'entropy = 0.0
samples = 2
value = [0, 0, 2]
class = virginica'),
17  Text(1030.1538461538462, 453.0, 'entropy = 0.0
samples = 43
value = [0, 0, 43]
class = virginica')]

    •  异常数据检测

     

     数据文件 anomaly_data.csv 内容为：

     

     

     

     

     

     

     

     

     

     

     

     

     

     1 #visualize the result 预测结果图形化展示
 2 #原始数据
 3 fig4 = plt.figure(figsize=(20,10))
 4 original_data = plt.scatter(data.loc[:,'x1'],data.loc[:,'x2'],marker='x')#参数3：数据标记
 5 anomaly_data = plt.scatter(data.loc[:,'x1'][y_predict==-1],data.loc[:,'x2'][y_predict==-1],marker='o',facecolor='none',edgecolor='red',s=150)#参数3：数据标记 参数4：颜色填充 参数5：边框颜色 参数6：尺寸
 6 plt.title('anomaly detection result')
 7 plt.xlabel('x1')
 8 plt.ylabel('x2')
 9 plt.legend((original_data,anomaly_data),('original_data','anomaly_data'))#题注设置
10 plt.show()

     1 #establish the model and predict 建立模型更改检测精度并预测
 2 ad_model = EllipticEnvelope(contamination=0.02)
 3 ad_model.fit(data)
 4 y_predict = ad_model.predict(data)
 5 
 6 #visualize the result 更改检测精度后预测结果图形化展示
 7 #原始数据
 8 fig5 = plt.figure(figsize=(20,10))
 9 original_data = plt.scatter(data.loc[:,'x1'],data.loc[:,'x2'],marker='x')#参数3：数据标记
10 anomaly_data = plt.scatter(data.loc[:,'x1'][y_predict==-1],data.loc[:,'x2'][y_predict==-1],marker='o',facecolor='none',edgecolor='red',s=150)#参数3：数据标记 参数4：颜色填充 参数5：边框颜色 参数6：尺寸
11 plt.title('anomaly detection result')
12 plt.xlabel('x1')
13 plt.ylabel('x2')
14 plt.legend((original_data,anomaly_data),('original_data','anomaly_data'))#题注设置
15 plt.show()

     

    • 主成分分析实战

     

     

     

     

    1 #establish knn model and calculate the accuracy 建立模型 训练模型 评估模型
2 from sklearn.neighbors import KNeighborsClassifier
3 KNN = KNeighborsClassifier(n_neighbors=3) #KNN分类器 ：参数1：取最近三个点作为分类
4 KNN.fit(x,y)
5 y_predict = KNN.predict(x)
6 from sklearn.metrics import accuracy_score
7 accuracy = accuracy_score(y,y_predict)
8 print(accuracy)

     

      1 [[-9.00681170e-01  1.03205722e+00 -1.34127240e+00 -1.31297673e+00]
  2  [-1.14301691e+00 -1.24957601e-01 -1.34127240e+00 -1.31297673e+00]
  3  [-1.38535265e+00  3.37848329e-01 -1.39813811e+00 -1.31297673e+00]
  4  [-1.50652052e+00  1.06445364e-01 -1.28440670e+00 -1.31297673e+00]
  5  [-1.02184904e+00  1.26346019e+00 -1.34127240e+00 -1.31297673e+00]
  6  [-5.37177559e-01  1.95766909e+00 -1.17067529e+00 -1.05003079e+00]
  7  [-1.50652052e+00  8.00654259e-01 -1.34127240e+00 -1.18150376e+00]
  8  [-1.02184904e+00  8.00654259e-01 -1.28440670e+00 -1.31297673e+00]
  9  [-1.74885626e+00 -3.56360566e-01 -1.34127240e+00 -1.31297673e+00]
 10  [-1.14301691e+00  1.06445364e-01 -1.28440670e+00 -1.44444970e+00]
 11  [-5.37177559e-01  1.49486315e+00 -1.28440670e+00 -1.31297673e+00]
 12  [-1.26418478e+00  8.00654259e-01 -1.22754100e+00 -1.31297673e+00]
 13  [-1.26418478e+00 -1.24957601e-01 -1.34127240e+00 -1.44444970e+00]
 14  [-1.87002413e+00 -1.24957601e-01 -1.51186952e+00 -1.44444970e+00]
 15  [-5.25060772e-02  2.18907205e+00 -1.45500381e+00 -1.31297673e+00]
 16  [-1.73673948e-01  3.11468391e+00 -1.28440670e+00 -1.05003079e+00]
 17  [-5.37177559e-01  1.95766909e+00 -1.39813811e+00 -1.05003079e+00]
 18  [-9.00681170e-01  1.03205722e+00 -1.34127240e+00 -1.18150376e+00]
 19  [-1.73673948e-01  1.72626612e+00 -1.17067529e+00 -1.18150376e+00]
 20  [-9.00681170e-01  1.72626612e+00 -1.28440670e+00 -1.18150376e+00]
 21  [-5.37177559e-01  8.00654259e-01 -1.17067529e+00 -1.31297673e+00]
 22  [-9.00681170e-01  1.49486315e+00 -1.28440670e+00 -1.05003079e+00]
 23  [-1.50652052e+00  1.26346019e+00 -1.56873522e+00 -1.31297673e+00]
 24  [-9.00681170e-01  5.69251294e-01 -1.17067529e+00 -9.18557817e-01]
 25  [-1.26418478e+00  8.00654259e-01 -1.05694388e+00 -1.31297673e+00]
 26  [-1.02184904e+00 -1.24957601e-01 -1.22754100e+00 -1.31297673e+00]
 27  [-1.02184904e+00  8.00654259e-01 -1.22754100e+00 -1.05003079e+00]
 28  [-7.79513300e-01  1.03205722e+00 -1.28440670e+00 -1.31297673e+00]
 29  [-7.79513300e-01  8.00654259e-01 -1.34127240e+00 -1.31297673e+00]
 30  [-1.38535265e+00  3.37848329e-01 -1.22754100e+00 -1.31297673e+00]
 31  [-1.26418478e+00  1.06445364e-01 -1.22754100e+00 -1.31297673e+00]
 32  [-5.37177559e-01  8.00654259e-01 -1.28440670e+00 -1.05003079e+00]
 33  [-7.79513300e-01  2.42047502e+00 -1.28440670e+00 -1.44444970e+00]
 34  [-4.16009689e-01  2.65187798e+00 -1.34127240e+00 -1.31297673e+00]
 35  [-1.14301691e+00  1.06445364e-01 -1.28440670e+00 -1.44444970e+00]
 36  [-1.02184904e+00  3.37848329e-01 -1.45500381e+00 -1.31297673e+00]
 37  [-4.16009689e-01  1.03205722e+00 -1.39813811e+00 -1.31297673e+00]
 38  [-1.14301691e+00  1.06445364e-01 -1.28440670e+00 -1.44444970e+00]
 39  [-1.74885626e+00 -1.24957601e-01 -1.39813811e+00 -1.31297673e+00]
 40  [-9.00681170e-01  8.00654259e-01 -1.28440670e+00 -1.31297673e+00]
 41  [-1.02184904e+00  1.03205722e+00 -1.39813811e+00 -1.18150376e+00]
 42  [-1.62768839e+00 -1.74477836e+00 -1.39813811e+00 -1.18150376e+00]
 43  [-1.74885626e+00  3.37848329e-01 -1.39813811e+00 -1.31297673e+00]
 44  [-1.02184904e+00  1.03205722e+00 -1.22754100e+00 -7.87084847e-01]
 45  [-9.00681170e-01  1.72626612e+00 -1.05694388e+00 -1.05003079e+00]
 46  [-1.26418478e+00 -1.24957601e-01 -1.34127240e+00 -1.18150376e+00]
 47  [-9.00681170e-01  1.72626612e+00 -1.22754100e+00 -1.31297673e+00]
 48  [-1.50652052e+00  3.37848329e-01 -1.34127240e+00 -1.31297673e+00]
 49  [-6.58345429e-01  1.49486315e+00 -1.28440670e+00 -1.31297673e+00]
 50  [-1.02184904e+00  5.69251294e-01 -1.34127240e+00 -1.31297673e+00]
 51  [ 1.40150837e+00  3.37848329e-01  5.35295827e-01  2.64698913e-01]
 52  [ 6.74501145e-01  3.37848329e-01  4.21564419e-01  3.96171883e-01]
 53  [ 1.28034050e+00  1.06445364e-01  6.49027235e-01  3.96171883e-01]
 54  [-4.16009689e-01 -1.74477836e+00  1.37235899e-01  1.33225943e-01]
 55  [ 7.95669016e-01 -5.87763531e-01  4.78430123e-01  3.96171883e-01]
 56  [-1.73673948e-01 -5.87763531e-01  4.21564419e-01  1.33225943e-01]
 57  [ 5.53333275e-01  5.69251294e-01  5.35295827e-01  5.27644853e-01]
 58  [-1.14301691e+00 -1.51337539e+00 -2.60824029e-01 -2.61192967e-01]
 59  [ 9.16836886e-01 -3.56360566e-01  4.78430123e-01  1.33225943e-01]
 60  [-7.79513300e-01 -8.19166497e-01  8.03701950e-02  2.64698913e-01]
 61  [-1.02184904e+00 -2.43898725e+00 -1.47092621e-01 -2.61192967e-01]
 62  [ 6.86617933e-02 -1.24957601e-01  2.50967307e-01  3.96171883e-01]
 63  [ 1.89829664e-01 -1.97618132e+00  1.37235899e-01 -2.61192967e-01]
 64  [ 3.10997534e-01 -3.56360566e-01  5.35295827e-01  2.64698913e-01]
 65  [-2.94841818e-01 -3.56360566e-01 -9.02269170e-02  1.33225943e-01]
 66  [ 1.03800476e+00  1.06445364e-01  3.64698715e-01  2.64698913e-01]
 67  [-2.94841818e-01 -1.24957601e-01  4.21564419e-01  3.96171883e-01]
 68  [-5.25060772e-02 -8.19166497e-01  1.94101603e-01 -2.61192967e-01]
 69  [ 4.32165405e-01 -1.97618132e+00  4.21564419e-01  3.96171883e-01]
 70  [-2.94841818e-01 -1.28197243e+00  8.03701950e-02 -1.29719997e-01]
 71  [ 6.86617933e-02  3.37848329e-01  5.92161531e-01  7.90590793e-01]
 72  [ 3.10997534e-01 -5.87763531e-01  1.37235899e-01  1.33225943e-01]
 73  [ 5.53333275e-01 -1.28197243e+00  6.49027235e-01  3.96171883e-01]
 74  [ 3.10997534e-01 -5.87763531e-01  5.35295827e-01  1.75297293e-03]
 75  [ 6.74501145e-01 -3.56360566e-01  3.07833011e-01  1.33225943e-01]
 76  [ 9.16836886e-01 -1.24957601e-01  3.64698715e-01  2.64698913e-01]
 77  [ 1.15917263e+00 -5.87763531e-01  5.92161531e-01  2.64698913e-01]
 78  [ 1.03800476e+00 -1.24957601e-01  7.05892939e-01  6.59117823e-01]
 79  [ 1.89829664e-01 -3.56360566e-01  4.21564419e-01  3.96171883e-01]
 80  [-1.73673948e-01 -1.05056946e+00 -1.47092621e-01 -2.61192967e-01]
 81  [-4.16009689e-01 -1.51337539e+00  2.35044910e-02 -1.29719997e-01]
 82  [-4.16009689e-01 -1.51337539e+00 -3.33612130e-02 -2.61192967e-01]
 83  [-5.25060772e-02 -8.19166497e-01  8.03701950e-02  1.75297293e-03]
 84  [ 1.89829664e-01 -8.19166497e-01  7.62758643e-01  5.27644853e-01]
 85  [-5.37177559e-01 -1.24957601e-01  4.21564419e-01  3.96171883e-01]
 86  [ 1.89829664e-01  8.00654259e-01  4.21564419e-01  5.27644853e-01]
 87  [ 1.03800476e+00  1.06445364e-01  5.35295827e-01  3.96171883e-01]
 88  [ 5.53333275e-01 -1.74477836e+00  3.64698715e-01  1.33225943e-01]
 89  [-2.94841818e-01 -1.24957601e-01  1.94101603e-01  1.33225943e-01]
 90  [-4.16009689e-01 -1.28197243e+00  1.37235899e-01  1.33225943e-01]
 91  [-4.16009689e-01 -1.05056946e+00  3.64698715e-01  1.75297293e-03]
 92  [ 3.10997534e-01 -1.24957601e-01  4.78430123e-01  2.64698913e-01]
 93  [-5.25060772e-02 -1.05056946e+00  1.37235899e-01  1.75297293e-03]
 94  [-1.02184904e+00 -1.74477836e+00 -2.60824029e-01 -2.61192967e-01]
 95  [-2.94841818e-01 -8.19166497e-01  2.50967307e-01  1.33225943e-01]
 96  [-1.73673948e-01 -1.24957601e-01  2.50967307e-01  1.75297293e-03]
 97  [-1.73673948e-01 -3.56360566e-01  2.50967307e-01  1.33225943e-01]
 98  [ 4.32165405e-01 -3.56360566e-01  3.07833011e-01  1.33225943e-01]
 99  [-9.00681170e-01 -1.28197243e+00 -4.31421141e-01 -1.29719997e-01]
100  [-1.73673948e-01 -5.87763531e-01  1.94101603e-01  1.33225943e-01]
101  [ 5.53333275e-01  5.69251294e-01  1.27454998e+00  1.71090158e+00]
102  [-5.25060772e-02 -8.19166497e-01  7.62758643e-01  9.22063763e-01]
103  [ 1.52267624e+00 -1.24957601e-01  1.21768427e+00  1.18500970e+00]
104  [ 5.53333275e-01 -3.56360566e-01  1.04708716e+00  7.90590793e-01]
105  [ 7.95669016e-01 -1.24957601e-01  1.16081857e+00  1.31648267e+00]
106  [ 2.12851559e+00 -1.24957601e-01  1.61574420e+00  1.18500970e+00]
107  [-1.14301691e+00 -1.28197243e+00  4.21564419e-01  6.59117823e-01]
108  [ 1.76501198e+00 -3.56360566e-01  1.44514709e+00  7.90590793e-01]
109  [ 1.03800476e+00 -1.28197243e+00  1.16081857e+00  7.90590793e-01]
110  [ 1.64384411e+00  1.26346019e+00  1.33141568e+00  1.71090158e+00]
111  [ 7.95669016e-01  3.37848329e-01  7.62758643e-01  1.05353673e+00]
112  [ 6.74501145e-01 -8.19166497e-01  8.76490051e-01  9.22063763e-01]
113  [ 1.15917263e+00 -1.24957601e-01  9.90221459e-01  1.18500970e+00]
114  [-1.73673948e-01 -1.28197243e+00  7.05892939e-01  1.05353673e+00]
115  [-5.25060772e-02 -5.87763531e-01  7.62758643e-01  1.57942861e+00]
116  [ 6.74501145e-01  3.37848329e-01  8.76490051e-01  1.44795564e+00]
117  [ 7.95669016e-01 -1.24957601e-01  9.90221459e-01  7.90590793e-01]
118  [ 2.24968346e+00  1.72626612e+00  1.67260991e+00  1.31648267e+00]
119  [ 2.24968346e+00 -1.05056946e+00  1.78634131e+00  1.44795564e+00]
120  [ 1.89829664e-01 -1.97618132e+00  7.05892939e-01  3.96171883e-01]
121  [ 1.28034050e+00  3.37848329e-01  1.10395287e+00  1.44795564e+00]
122  [-2.94841818e-01 -5.87763531e-01  6.49027235e-01  1.05353673e+00]
123  [ 2.24968346e+00 -5.87763531e-01  1.67260991e+00  1.05353673e+00]
124  [ 5.53333275e-01 -8.19166497e-01  6.49027235e-01  7.90590793e-01]
125  [ 1.03800476e+00  5.69251294e-01  1.10395287e+00  1.18500970e+00]
126  [ 1.64384411e+00  3.37848329e-01  1.27454998e+00  7.90590793e-01]
127  [ 4.32165405e-01 -5.87763531e-01  5.92161531e-01  7.90590793e-01]
128  [ 3.10997534e-01 -1.24957601e-01  6.49027235e-01  7.90590793e-01]
129  [ 6.74501145e-01 -5.87763531e-01  1.04708716e+00  1.18500970e+00]
130  [ 1.64384411e+00 -1.24957601e-01  1.16081857e+00  5.27644853e-01]
131  [ 1.88617985e+00 -5.87763531e-01  1.33141568e+00  9.22063763e-01]
132  [ 2.49201920e+00  1.72626612e+00  1.50201279e+00  1.05353673e+00]
133  [ 6.74501145e-01 -5.87763531e-01  1.04708716e+00  1.31648267e+00]
134  [ 5.53333275e-01 -5.87763531e-01  7.62758643e-01  3.96171883e-01]
135  [ 3.10997534e-01 -1.05056946e+00  1.04708716e+00  2.64698913e-01]
136  [ 2.24968346e+00 -1.24957601e-01  1.33141568e+00  1.44795564e+00]
137  [ 5.53333275e-01  8.00654259e-01  1.04708716e+00  1.57942861e+00]
138  [ 6.74501145e-01  1.06445364e-01  9.90221459e-01  7.90590793e-01]
139  [ 1.89829664e-01 -1.24957601e-01  5.92161531e-01  7.90590793e-01]
140  [ 1.28034050e+00  1.06445364e-01  9.33355755e-01  1.18500970e+00]
141  [ 1.03800476e+00  1.06445364e-01  1.04708716e+00  1.57942861e+00]
142  [ 1.28034050e+00  1.06445364e-01  7.62758643e-01  1.44795564e+00]
143  [-5.25060772e-02 -8.19166497e-01  7.62758643e-01  9.22063763e-01]
144  [ 1.15917263e+00  3.37848329e-01  1.21768427e+00  1.44795564e+00]
145  [ 1.03800476e+00  5.69251294e-01  1.10395287e+00  1.71090158e+00]
146  [ 1.03800476e+00 -1.24957601e-01  8.19624347e-01  1.44795564e+00]
147  [ 5.53333275e-01 -1.28197243e+00  7.05892939e-01  9.22063763e-01]
148  [ 7.95669016e-01 -1.24957601e-01  8.19624347e-01  1.05353673e+00]
149  [ 4.32165405e-01  8.00654259e-01  9.33355755e-01  1.44795564e+00]
150  [ 6.86617933e-02 -1.24957601e-01  7.62758643e-01  7.90590793e-01]]

     

     

     

    1 #visualize the PCA result 缩减后(降维数据)pca图形展示
2 fig3 = plt.figure(figsize=(10,10))
3 setosa = plt.scatter(x_pca[:,0][y==0],x_pca[:,1][y==0])
4 versicolor = plt.scatter(x_pca[:,0][y==1],x_pca[:,1][y==1])
5 virginica = plt.scatter(x_pca[:,0][y==2],x_pca[:,1][y==2])
6 plt.legend((setosa,versicolor,virginica),('setosa','versicolor','virginica'))
7 plt.show()

    1 #降维后建立KNN模型且查看表现
2 KNN = KNeighborsClassifier(n_neighbors=3) #KNN分类器 ：参数1：取最近三个点作为分类
3 KNN.fit(x_pca,y)
4 y_predict = KNN.predict(x_pca)
5 from sklearn.metrics import accuracy_score
6 accuracy = accuracy_score(y,y_predict)
7 print(accuracy)

     
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  • 原文地址：https://www.cnblogs.com/liuxiaoming123/p/13667573.html
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