從百度雲課堂上截圖的基礎概念，如果之前不了解的可以先看一下這篇部落格
不同的資料集訓練不同的模型，根據模型進行投票得到最終預測結果
多棵決策樹組成森林，每個模型訓練集不同和選擇的決策屬性不同是RF算法随機的最主要展現
adaboost算法不同模型之間會有影響
多層分類器進行結果的預測
bagging算法提高KNN和決策樹算法精确度
1 # 導入算法包以及資料集
2 from sklearn import neighbors
3 from sklearn import datasets
4 from sklearn.ensemble import BaggingClassifier
5 from sklearn import tree
6 from sklearn.model_selection import train_test_split
7 import numpy as np
8 import matplotlib.pyplot as plt
9 iris = datasets.load_iris()
10 x_data = iris.data[:,:2]
11 y_data = iris.target
12 def plot(model):
13 # 擷取資料值所在的範圍
14 x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
15 y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
16
17 # 生成網格矩陣
18 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
19 np.arange(y_min, y_max, 0.02))
20
21 z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel與flatten類似，多元資料轉一維。flatten不會改變原始資料，ravel會改變原始資料
22 z = z.reshape(xx.shape)
23 # 等高線圖
24 cs = plt.contourf(xx, yy, z)
25 x_train,x_test,y_train,y_test = train_test_split(x_data, y_data)
26 knn = neighbors.KNeighborsClassifier()
27 knn.fit(x_train, y_train)
28 # 畫圖
29 plot(knn)
30 # 樣本散點圖
31 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
32 plt.show()
33 # 準确率
34 print("knn:",knn.score(x_test, y_test))
35 dtree = tree.DecisionTreeClassifier()
36 dtree.fit(x_train, y_train)
37 # 畫圖
38 plot(dtree)
39 # 樣本散點圖
40 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
41 plt.show()
42 # 準确率
43 print("dtree:",dtree.score(x_test, y_test))
44 bagging_knn = BaggingClassifier(knn, n_estimators=100)#使用bagging算法訓練100組knn分類器
45 # 輸入資料建立模型
46 bagging_knn.fit(x_train, y_train)
47 plot(bagging_knn)
48 # 樣本散點圖
49 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
50 plt.show()
51 print("bagging:",bagging_knn.score(x_test, y_test))
52 bagging_tree = BaggingClassifier(dtree, n_estimators=100)#使用bagging算法訓練100組決策樹分類器
53 # 輸入資料建立模型
54 bagging_tree.fit(x_train, y_train)
55 plot(bagging_tree)
56 # 樣本散點圖
57 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
58 plt.show()
59 print("bagging_tree",bagging_tree.score(x_test, y_test)) 随機森林對決策樹模型優化
1 from sklearn import tree
2 from sklearn.model_selection import train_test_split
3 from sklearn.ensemble import RandomForestClassifier
4 import numpy as np
5 import matplotlib.pyplot as plt
6 # 載入資料
7 data = np.genfromtxt("LR-testSet2.txt", delimiter=",")
8 x_data = data[:,:-1]
9 y_data = data[:,-1]
10
11 plt.scatter(x_data[:,0],x_data[:,1],c=y_data)
12 plt.show()
13 x_train,x_test,y_train,y_test = train_test_split(x_data, y_data, test_size = 0.5)
14 def plot(model):
15 # 擷取資料值所在的範圍
16 x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
17 y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
18
19 # 生成網格矩陣
20 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
21 np.arange(y_min, y_max, 0.02))
22
23 z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel與flatten類似，多元資料轉一維。flatten不會改變原始資料，ravel會改變原始資料
24 z = z.reshape(xx.shape)
25 # 等高線圖
26 cs = plt.contourf(xx, yy, z)
27 # 樣本散點圖
28 plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test)
29 plt.show()
30 dtree = tree.DecisionTreeClassifier()
31 dtree.fit(x_train, y_train)
32 plot(dtree)
33 print("dtree",dtree.score(x_test, y_test))
34 RF = RandomForestClassifier(n_estimators=50)
35 RF.fit(x_train, y_train)
36 plot(RF)
37 print("RandomForest",RF.score(x_test, y_test)) 下面是用到的資料
1 0.051267,0.69956,1
2 -0.092742,0.68494,1
3 -0.21371,0.69225,1
4 -0.375,0.50219,1
5 -0.51325,0.46564,1
6 -0.52477,0.2098,1
7 -0.39804,0.034357,1
8 -0.30588,-0.19225,1
9 0.016705,-0.40424,1
10 0.13191,-0.51389,1
11 0.38537,-0.56506,1
12 0.52938,-0.5212,1
13 0.63882,-0.24342,1
14 0.73675,-0.18494,1
15 0.54666,0.48757,1
16 0.322,0.5826,1
17 0.16647,0.53874,1
18 -0.046659,0.81652,1
19 -0.17339,0.69956,1
20 -0.47869,0.63377,1
21 -0.60541,0.59722,1
22 -0.62846,0.33406,1
23 -0.59389,0.005117,1
24 -0.42108,-0.27266,1
25 -0.11578,-0.39693,1
26 0.20104,-0.60161,1
27 0.46601,-0.53582,1
28 0.67339,-0.53582,1
29 -0.13882,0.54605,1
30 -0.29435,0.77997,1
31 -0.26555,0.96272,1
32 -0.16187,0.8019,1
33 -0.17339,0.64839,1
34 -0.28283,0.47295,1
35 -0.36348,0.31213,1
36 -0.30012,0.027047,1
37 -0.23675,-0.21418,1
38 -0.06394,-0.18494,1
39 0.062788,-0.16301,1
40 0.22984,-0.41155,1
41 0.2932,-0.2288,1
42 0.48329,-0.18494,1
43 0.64459,-0.14108,1
44 0.46025,0.012427,1
45 0.6273,0.15863,1
46 0.57546,0.26827,1
47 0.72523,0.44371,1
48 0.22408,0.52412,1
49 0.44297,0.67032,1
50 0.322,0.69225,1
51 0.13767,0.57529,1
52 -0.0063364,0.39985,1
53 -0.092742,0.55336,1
54 -0.20795,0.35599,1
55 -0.20795,0.17325,1
56 -0.43836,0.21711,1
57 -0.21947,-0.016813,1
58 -0.13882,-0.27266,1
59 0.18376,0.93348,0
60 0.22408,0.77997,0
61 0.29896,0.61915,0
62 0.50634,0.75804,0
63 0.61578,0.7288,0
64 0.60426,0.59722,0
65 0.76555,0.50219,0
66 0.92684,0.3633,0
67 0.82316,0.27558,0
68 0.96141,0.085526,0
69 0.93836,0.012427,0
70 0.86348,-0.082602,0
71 0.89804,-0.20687,0
72 0.85196,-0.36769,0
73 0.82892,-0.5212,0
74 0.79435,-0.55775,0
75 0.59274,-0.7405,0
76 0.51786,-0.5943,0
77 0.46601,-0.41886,0
78 0.35081,-0.57968,0
79 0.28744,-0.76974,0
80 0.085829,-0.75512,0
81 0.14919,-0.57968,0
82 -0.13306,-0.4481,0
83 -0.40956,-0.41155,0
84 -0.39228,-0.25804,0
85 -0.74366,-0.25804,0
86 -0.69758,0.041667,0
87 -0.75518,0.2902,0
88 -0.69758,0.68494,0
89 -0.4038,0.70687,0
90 -0.38076,0.91886,0
91 -0.50749,0.90424,0
92 -0.54781,0.70687,0
93 0.10311,0.77997,0
94 0.057028,0.91886,0
95 -0.10426,0.99196,0
96 -0.081221,1.1089,0
97 0.28744,1.087,0
98 0.39689,0.82383,0
99 0.63882,0.88962,0
100 0.82316,0.66301,0
101 0.67339,0.64108,0
102 1.0709,0.10015,0
103 -0.046659,-0.57968,0
104 -0.23675,-0.63816,0
105 -0.15035,-0.36769,0
106 -0.49021,-0.3019,0
107 -0.46717,-0.13377,0
108 -0.28859,-0.060673,0
109 -0.61118,-0.067982,0
110 -0.66302,-0.21418,0
111 -0.59965,-0.41886,0
112 -0.72638,-0.082602,0
113 -0.83007,0.31213,0
114 -0.72062,0.53874,0
115 -0.59389,0.49488,0
116 -0.48445,0.99927,0
117 -0.0063364,0.99927,0
118 0.63265,-0.030612,0 View Code
adaboost決策樹
1 import numpy as np
2 import matplotlib.pyplot as plt
3 from sklearn import tree
4 from sklearn.ensemble import AdaBoostClassifier
5 from sklearn.tree import DecisionTreeClassifier
6 from sklearn.datasets import make_gaussian_quantiles
7 from sklearn.metrics import classification_report
8 # 生成2維正态分布，生成的資料按分位數分為兩類，500個樣本,2個樣本特征
9 x1, y1 = make_gaussian_quantiles(n_samples=500, n_features=2,n_classes=2)#預設mean=(0, 0)
10 # 生成2維正态分布，生成的資料按分位數分為兩類，400個樣本,2個樣本特征均值都為3
11 x2, y2 = make_gaussian_quantiles(mean=(3, 3), n_samples=500, n_features=2, n_classes=2)
12 # 将兩組資料合成一組資料
13 x_data = np.concatenate((x1, x2))
14 y_data = np.concatenate((y1, - y2 + 1))
15 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
16 plt.show()
17 # 決策樹模型
18 model = tree.DecisionTreeClassifier(max_depth=3)
19
20 # 輸入資料建立模型
21 model.fit(x_data, y_data)
22
23 # 擷取資料值所在的範圍
24 x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
25 y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
26
27 # 生成網格矩陣
28 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
29 np.arange(y_min, y_max, 0.02))
30
31 z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel與flatten類似，多元資料轉一維。flatten不會改變原始資料，ravel會改變原始資料
32 z = z.reshape(xx.shape)
33 # 等高線圖
34 cs = plt.contourf(xx, yy, z)
35 # 樣本散點圖
36 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
37 plt.show()
38 # 模型準确率
39 print("決策樹：",model.score(x_data,y_data))
40 # AdaBoost模型
41 model = AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),n_estimators=10)#決策樹深度為3，一共訓練十個模型
42 # 訓練模型
43 model.fit(x_data, y_data)
44
45 # 擷取資料值所在的範圍
46 x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
47 y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
48
49 # 生成網格矩陣
50 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
51 np.arange(y_min, y_max, 0.02))
52
53 # 擷取預測值
54 z = model.predict(np.c_[xx.ravel(), yy.ravel()])
55 z = z.reshape(xx.shape)
56 # 等高線圖
57 cs = plt.contourf(xx, yy, z)
58 # 樣本散點圖
59 plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
60 plt.show()
61 # 模型準确率
62 print("adaboost：",model.score(x_data,y_data))#得分很高 stacking分類器算法
1 from sklearn import datasets
2 from sklearn import model_selection
3 from sklearn.linear_model import LogisticRegression
4 from sklearn.neighbors import KNeighborsClassifier
5 from sklearn.tree import DecisionTreeClassifier
6 from mlxtend.classifier import StackingClassifier # pip install mlxtend
7 import numpy as np
8
9 # 載入資料集
10 iris = datasets.load_iris()
11 # 隻要第1,2列的特征
12 x_data, y_data = iris.data[:, 1:3], iris.target
13
14 # 定義三個不同的分類器
15 clf1 = KNeighborsClassifier(n_neighbors=1)
16 clf2 = DecisionTreeClassifier()
17 clf3 = LogisticRegression()
18
19 # 定義一個次級分類器
20 lr = LogisticRegression()
21 sclf = StackingClassifier(classifiers=[clf1, clf2, clf3],
22 meta_classifier=lr)
23
24 for clf, label in zip([clf1, clf2, clf3, sclf],
25 ['KNN', 'Decision Tree', 'LogisticRegression', 'StackingClassifier']):
26 scores = model_selection.cross_val_score(clf, x_data, y_data, cv=3, scoring='accuracy')
27 print("Accuracy: %0.2f [%s]" % (scores.mean(), label)) voting分類器
1 from sklearn import datasets
2 from sklearn import model_selection
3 from sklearn.linear_model import LogisticRegression
4 from sklearn.neighbors import KNeighborsClassifier
5 from sklearn.tree import DecisionTreeClassifier
6 from sklearn.ensemble import VotingClassifier
7 import numpy as np
8
9 # 載入資料集
10 iris = datasets.load_iris()
11 # 隻要第1,2列的特征
12 x_data, y_data = iris.data[:, 1:3], iris.target
13
14 # 定義三個不同的分類器
15 clf1 = KNeighborsClassifier(n_neighbors=1)
16 clf2 = DecisionTreeClassifier()
17 clf3 = LogisticRegression()
18
19 sclf = VotingClassifier([('knn', clf1), ('dtree', clf2), ('lr', clf3)])
20
21 for clf, label in zip([clf1, clf2, clf3, sclf],
22 ['KNN', 'Decision Tree', 'LogisticRegression', 'VotingClassifier']):
23 scores = model_selection.cross_val_score(clf, x_data, y_data, cv=3, scoring='accuracy')
24 print("Accuracy: %0.2f [%s]" % (scores.mean(), label))