用knn演算法對鳶尾花資料集進行分類

2021-10-12 19:12:27 字數 3113 閱讀 2463

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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