sklearn中有很多經典分類器，使用非常簡單：1.導入數據 2.導入模型 3.fit--->predict

下面的示例為在iris數據集上用各種分類器進行分類：

 1 #用各種方式在iris數據集上數據分類
 2 
 3 #載入iris數據集，其中每個特征向量有四個維度，有三種類別
 4 from sklearn import datasets
 5 iris = datasets.load_iris()
 6 print ("The iris‘ target names: ",iris.target_names)
 7 x = iris.data
 8 y = iris.target
 
 9 
10 #待分類的兩個樣本
11 test_vector = [[1,-1,2.6,-2],[0,0,7,0.8]]
12 
13 #線性回歸
14 from sklearn import linear_model
15 linear = linear_model.LinearRegression()
16 linear.fit(x,y)
17 print ("linear‘s score: ",linear.score(x,y))
18 print ("w:",linear.coef_)       
19 print ("b:",linear.intercept_)  
20 print ("
 
predict: ",linear.predict(test_vector))
21 
22 #邏輯回歸
23 LR = linear_model.LogisticRegression()
24 LR.fit(x,y)
25 print ("LogisticRegression:",LR.predict(test_vector))
26 
27 #決策樹
28 from sklearn import tree
29 TR = tree.DecisionTreeClassifier(criterion=‘entropy‘)   
30 TR.fit(x,y)
31 print ("DecisionTree:
 
",TR.predict(test_vector))
32 
33 #支持向量機
34 from sklearn import svm
35 SV = svm.SVC()
36 SV.fit(x,y)
37 print ("svm:",SV.predict(test_vector))
38 
39 #樸素貝葉斯
40 from sklearn import naive_bayes
41 NB = naive_bayes.GaussianNB()
42 NB.fit(x,y)
43 print ("naive_bayes:",NB.predict(test_vector))
44 
45 #K近鄰
46 from sklearn import neighbors
47 KNN = neighbors.KNeighborsClassifier(n_neighbors = 3)
48 KNN.fit(x,y)
49 print ("KNeighbors:",KNN.predict(test_vector))
50 ‘‘‘
51 he iris‘ target names:  [‘setosa‘ ‘versicolor‘ ‘virginica‘]
52 linear‘s score:  0.930422367533
53 w: [-0.10974146 -0.04424045  0.22700138  0.60989412]
54 b: 0.192083994828
55 predict:  [-0.50300167  2.26900897]
56 LogisticRegression: [1 2]
57 DecisionTree: [1 2]
58 svm: [2 2]
59 naive_bayes: [2 2]
60 KNeighbors: [0 1]
61 ‘‘‘

sklearn各種分類器簡單使用