Use the knn algorithm to classify the iris data set

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier

def knn_selector():
    
    iris = load_iris()
    
    x_train,x_test,y_train,y_test = train_test_split(iris.data, iris.target, test_size=0.3)
    
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)
    
    estimator = KNeighborsClassifier(n_neighbors = 3)
    estimator.fit(x_train, y_train)
    
#      estimator.predict(x_test) 
    score = estimator.score(x_test, y_test)
    
    print("score:  ", score)
    
    
if __name__ == "__main__":
    
    knn_selector()

import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler

# 获取数据集
# 划分数据集
# 标准化
# 创建模型
# 模型训练
# 模型预测与评估

def knn_selector():
    
    iris = load_iris()
    
    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size= 0.3)
    
#     print(X_train)
#     plt.plot(X_train[:,0])
#     plt.show()
    
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)
    
#     print(X_train[:,0])
#     plt.plot(X_train[:,0])
#     plt.show()
    
    estimator = KNeighborsClassifier(n_neighbors = 3)
    estimator.fit(X_train, y_train)
    
    score = estimator.score(X_test, y_test)
    
    print("准确率：  ", score)

if __name__ == "__main__":
    
    knn_selector()

from sklearn import datasets 
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

#------------------------------引入数据------------------------------

iris = datasets.load_iris() #  引入 iris 鸢尾花数据集
# 鸢尾花数据集 包含 4个 特征变量 

iris_X = iris.data   # 特征变量
iris_y = iris.target  # 目标值

# iris['data']
# iris['target']

X_train, X_test, y_train, y_test = train_test_split(iris_X, iris_y, test_size=0.3)

#---------------------------训练数据
knn = KNeighborsClassifier()   # 引入训练方法
knn.fit(X_train,y_train)  # 进行填充测试数据进行训练

knn.predict(X_test)  # 预测 特征值

'''
array([2, 1, 2, 0, 0, 1, 1, 2, 0, 1, 0, 2, 0, 1, 0, 2, 1, 2, 2, 2, 2, 2, 1,
       1, 1, 1, 0, 2, 1, 2, 0, 1, 1, 0, 0, 2, 0, 0, 1, 0, 2, 1, 1, 2, 2])

'''

y_test # 真实的 特征值
'''
array([2, 1, 2, 0, 0, 1, 2, 2, 0, 1, 0, 2, 0, 1, 0, 2, 1, 2, 2, 2, 2, 2, 1,
       1, 1, 2, 0, 2, 2, 2, 0, 1, 1, 0, 0, 2, 0, 0, 1, 0, 1, 1, 1, 2, 2])
'''

print(test_y)

print(pre)

print( sum(abs(pre - test_y)) / len(pre) )

knn.score(test_X, test_y)

Use the knn algorithm to classify the iris data set

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