机器学习---k近邻 笔记

1.读取Iris数据集细节资料

from sklearn.datasets import load_iris

iris = load_iris()
print iris.data.shape

查看数据说明

print iris.DESCR

2.对Iris数据集进行分割

from sklearn.cross_validation import train_test_split
x_train,x_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.25,random_state=33)

3.使用K近邻分类器对Iris数据进行类型预测

from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier

ss = StandardScaler()
x_train = ss.fit_transform(x_train)
x_test = ss.transform(x_test)

knc = KNeighborsClassifier()
knc.fit(x_train,y_train)
y_predict = knc.predict(x_test)

4.对K近邻分类器在Iris数据上的预测性能进行评估

print "accuracy is: ",knc.score(x_test,y_test)

from sklearn.metrics import classification_report
print classification_report(y_test,y_predict,target_names=iris.target_names)

5.输出

accuracy is:  0.894736842105
             precision    recall  f1-score   support

     setosa       1.00      1.00      1.00         8
 versicolor       0.73      1.00      0.85        11
  virginica       1.00      0.79      0.88        19

avg / total       0.92      0.89      0.90        38