用knn演算法對鳶尾花資料集進行分類

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
# 獲取資料集
# 劃分資料集
# 標準化
# 建立模型
# 模型訓練
# 模型**與評估
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("準確率： ", score)
if __name__ == "__main__":
knn_selector()

from sklearn import datasets 

from sklearn.model_selection import train_test_split
from sklearn.neighbors import kneighborsclassifier
#------------------------------引入資料------------------------------
iris = datasets.load_iris() # 引入 iris 鳶尾花資料集
# 鳶尾花資料集 包含 4個 特徵變數 
iris_x = iris.data # 特徵變數
iris_y = iris.target # 目標值
# iris['data']
# iris['target']
x_train, x_test, y_train, y_test = train_test_split(iris_x, iris_y, test_size=0.3)
#---------------------------訓練資料
knn = kneighborsclassifier() # 引入訓練方法
knn.fit(x_train,y_train) # 進行填充測試資料進行訓練
knn.predict(x_test) # ** 特徵值
'''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 # 真實的 特徵值
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