knn算法下载iris.csv文件,随机选取三分之一的数据作为测试数据,其他作为训练数据,估算出其最佳的K值。
时间: 2023-05-11 19:04:18 浏览: 59
可以使用Python中的scikit-learn库来实现KNN算法。以下是实现代码:
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
# 读取数据
data = pd.read_csv('iris.csv')
# 分离特征和标签
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
# 估算最佳的K值
k_values = range(1, 26)
accuracy_scores = []
for k in k_values:
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_scores.append(accuracy_score(y_test, y_pred))
best_k = k_values[accuracy_scores.index(max(accuracy_scores))]
print("最佳的K值为:", best_k)
```
注意:这里的iris.csv文件需要与代码文件在同一目录下。