用KNN实现iris的4分类问题&测试精度

import matplotlib.pyplot as plt
from scipy import sparse
import numpy as np
import matplotlib as mt
import pandas as pd
from IPython.display import display
from sklearn.datasets import load_iris
import sklearn as sk
from sklearn.model_selection import  train_test_split
from sklearn.neighbors import KNeighborsClassifier

iris=load_iris()
#print(iris)
X_train,X_test,y_train,y_test = train_test_split(iris['data'],iris['target'],random_state=0)
iris_dataframe = pd.DataFrame(X_train,columns=iris.feature_names)
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train,y_train)
# KNeighborsClassifier(algorithm='auto',leaf_size=30,metric='minkowski',
#                      metric_params=None,n_jobs=1,n_neighbors=1,p=2,weights='uniform')
X_new = np.array([[5,2.9,1,0.2]])
print("X_new.shape:{}".format(X_new.shape))
prediction = knn.predict(X_new)
print("Prediction X_new:{}".format(prediction))
print("prediction X_new belong to {}".format(iris['target_names'][prediction]))

#评估模型
#计算精度方法1
print("test score1:{:.2f}".format(knn.score(X_test,y_test)))
#计算精度方法2
y_pred = knn.predict(X_test)
print("test score2:{:.2f}".format(np.mean(y_pred == y_test)))

输出：

Prediction X_new:[0]
prediction X_new belong to ['setosa']
test score1:0.97
test score2:0.97

 测试精度

knn的邻居设置会影响测试精度，举例说明：

import matplotlib.pyplot as plt
import mglearn
from scipy import sparse
import numpy as np
import matplotlib as mt
import pandas as pd
from IPython.display import display
from sklearn.datasets import load_breast_cancer
import sklearn as sk
from sklearn.model_selection import  train_test_split
from sklearn.neighbors import KNeighborsClassifier

cancer = load_breast_cancer()
X_train,X_test,y_train,y_test =train_test_split(cancer.data,cancer.target,stratify=cancer.target,random_state=66)
training_accuracy=[]
test_accuracy=[]
neighbors_settings = range(1,11)
for n_neighbors in neighbors_settings:
    clf = KNeighborsClassifier(n_neighbors=n_neighbors)
    clf.fit(X_train,y_train)
    training_accuracy.append(clf.score(X_train,y_train))
    test_accuracy.append(clf.score(X_test,y_test))

plt.plot(neighbors_settings,training_accuracy,label="training accuracy")
plt.plot(neighbors_settings,test_accuracy,label="test accuracy")
plt.xlabel("n_neighbors")
plt.ylabel("accuracy")
plt.legend()
plt.show()

可以看出，6是最优。

KNN算法的优点是简单可解释性强，

缺点是：

• 样本大的时候性能不好
• 特征多（几百个+）的时候效果不好
• 稀疏数据集不适用