使用KNeighborsClassifier对load_wine数据集进行训练并预测

• 1.导入load_wine数据集

from sklearn.datasets import load_wine

# data是一种bunch对象 含有键值对
data = load_wine()
# print(data)
print(data.keys())
# print(data['data'].shape)
# print(data['DESCR'])

• 2.导入模型并训练数据集。

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

X_train, X_test, y_train, y_test = train_test_split(data['data'], data['target'], random_state=0)


knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)
print(knn)
print("测试数据及得分：", knn.score(X_test, y_test))

• 3.对数据集进行预测。

import numpy as np

# 预测
X_new = np.array([[13.2, 2.77, 2.51, 18.5, 96.6, 1.04, 2.55, 1057, 1.47, 6.2, 1.05, 3.33, 820]])
prediction = knn.predict(X_new)
print("改数据集的红酒分类：", data['target_names'][prediction])