利用k-means算法完成Iris数据集的聚类,并输出聚类结果的正确率和召回率,并写出代码...
时间: 2024-03-21 21:42:40 浏览: 41
好的,我来回答你的问题。
首先,我们需要导入相关的库和数据集。这里我们使用sklearn库中的Iris数据集。
```python
import numpy as np
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score, recall_score
iris = datasets.load_iris()
X = iris.data
y = iris.target
```
接下来,我们可以使用KMeans算法进行聚类。这里我们假设数据集有3个类别。
```python
kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
labels = kmeans.labels_
```
得到聚类结果后,我们可以计算正确率和召回率。首先,我们需要将聚类结果和真实标签之间的映射关系确定下来。这里我们使用众数来确定映射关系。
```python
label_map = {}
for i in range(3):
temp_y = y[labels == i]
label_map[i] = np.bincount(temp_y).argmax()
```
然后,我们可以使用映射关系将聚类结果转换为真实标签,并计算正确率和召回率。
```python
pred_y = np.array([label_map[label] for label in labels])
accuracy = accuracy_score(y, pred_y)
recall = recall_score(y, pred_y, average='macro')
print('Accuracy:', accuracy)
print('Recall:', recall)
```
完整代码如下:
```python
import numpy as np
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score, recall_score
iris = datasets.load_iris()
X = iris.data
y = iris.target
kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
labels = kmeans.labels_
label_map = {}
for i in range(3):
temp_y = y[labels == i]
label_map[i] = np.bincount(temp_y).argmax()
pred_y = np.array([label_map[label] for label in labels])
accuracy = accuracy_score(y, pred_y)
recall = recall_score(y, pred_y, average='macro')
print('Accuracy:', accuracy)
print('Recall:', recall)
```
希望能对你有所帮助!