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