ricky10116r2d2的部落格

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

iris = datasets.load_iris()  
iris_X = iris.data                # (150,4)  有150個資料，每個資料有4個屬性
iris_y = iris.target              # (150,)   共有150個結果 對應到 3 種花，用0 1 2 表示

##print(iris_X[:2, :])
##print(iris_y)

X_train, X_test, y_train, y_test = train_test_split(
iris_X, iris_y, test_size=0.3)       # 訓練 70% 測試 30%
                                                # x = 輸入 y = 輸出
##print(y_train)

knn = KNeighborsClassifier()        # 使用模塊 !!
knn.fit(X_train, y_train)                  # 學習
print(knn.predict(X_test))              # 預測結果
print(y_test)                                   # 真實結果

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參考

https://morvanzhou.github.io/tutorials/machine-learning/sklearn/2-2-general-pattern/

https://www.cnblogs.com/pinard/p/6065607.html