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]
'''