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 score1:0.97test 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