knn识别果蔬具体代码
时间: 2023-09-30 16:05:03 浏览: 38
以下是使用Python中的sklearn库实现knn识别果蔬的代码:
```python
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import pandas as pd
# 读取数据并进行预处理
data = pd.read_csv('fruits.csv')
label = data['fruit_name']
features = data[['mass', 'width', 'height', 'color_score']]
scaler = StandardScaler()
scaled_features = scaler.fit_transform(features)
# 划分训练集和测试集
train_features, test_features, train_label, test_label = train_test_split(scaled_features, label, test_size=0.3)
# 训练KNN分类器
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(train_features, train_label)
# 测试KNN分类器
accuracy = knn.score(test_features, test_label)
print('Accuracy:', accuracy)
```
其中,我们使用了Pandas库来读取CSV文件中的数据,并使用StandardScaler对数据进行标准化处理。然后,我们使用train_test_split将数据划分为训练集和测试集,并使用KNeighborsClassifier训练KNN分类器。最后,我们使用score方法评估KNN分类器的准确率。