用KNN实现iris的4分类问题

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