基于鸢尾花数据集训练一个 k近邻 模型
时间: 2024-05-13 20:14:25 浏览: 11
好的,我可以为您提供一个基于鸢尾花数据集训练 k 近邻模型的简单示例代码。我们可以使用 Scikit-learn 库中的 KNeighborsClassifier 类来实现。
首先,我们需要导入必要的库和数据集:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
X = iris.data
y = iris.target
```
然后我们将数据集分成训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
接下来,我们可以初始化 k 近邻模型,并使用训练集来拟合模型:
```python
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
```
最后,我们可以使用测试集来评估模型的性能:
```python
accuracy = knn.score(X_test, y_test)
print(f"Accuracy: {accuracy}")
```
完整的代码如下:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
accuracy = knn.score(X_test, y_test)
print(f"Accuracy: {accuracy}")
```
希望这个示例代码能对您有所帮助!