sklearn各种分类器简单使用

sklearn中有很多经典分类器，使用非常简单：1.导入数据 2.导入模型 3.fit--->predict

下面的示例为在iris数据集上用各种分类器进行分类：

 #用各种方式在iris数据集上数据分类

 #载入iris数据集，其中每个特征向量有四个维度，有三种类别
 from sklearn import datasets
 iris = datasets.load_iris()
 print ("The iris' target names: ",iris.target_names)
 x = iris.data
 y = iris.target

 #待分类的两个样本
 test_vector = [[1,-1,2.6,-2],[0,0,7,0.8]]

 #线性回归
 from sklearn import linear_model
 linear = linear_model.LinearRegression()
 linear.fit(x,y)
 print ("linear's score: ",linear.score(x,y))
 print ("w:",linear.coef_)
 print ("b:",linear.intercept_)
 print ("predict: ",linear.predict(test_vector))

 #逻辑回归
 LR = linear_model.LogisticRegression()
 LR.fit(x,y)
 print ("LogisticRegression:",LR.predict(test_vector))

 #决策树
 from sklearn import tree
 TR = tree.DecisionTreeClassifier(criterion='entropy')
 TR.fit(x,y)
 print ("DecisionTree:",TR.predict(test_vector))

 #支持向量机
 from sklearn import svm
 SV = svm.SVC()
 SV.fit(x,y)
 print ("svm:",SV.predict(test_vector))

 #朴素贝叶斯
 from sklearn import naive_bayes
 NB = naive_bayes.GaussianNB()
 NB.fit(x,y)
 print ("naive_bayes:",NB.predict(test_vector))

 #K近邻
 from sklearn import neighbors
 KNN = neighbors.KNeighborsClassifier(n_neighbors = 3)
 KNN.fit(x,y)
 print ("KNeighbors:",KNN.predict(test_vector))
 '''
 he iris' target names:  ['setosa' 'versicolor' 'virginica']
 linear's score:  0.930422367533
 w: [-0.10974146 -0.04424045  0.22700138  0.60989412]
 b: 0.192083994828
 predict:  [-0.50300167  2.26900897]
 LogisticRegression: [1 2]
 DecisionTree: [1 2]
 svm: [2 2]
 naive_bayes: [2 2]
 KNeighbors: [0 1]
 '''