python中knn算法预测鸢尾花数据集精度代码
时间: 2024-09-10 09:00:34 浏览: 23
在Python中,我们可以使用sklearn库中的K-近邻(KNN)算法对鸢尾花数据集进行预测,并计算预测精度。首先,你需要导入所需的库并加载数据集。以下是基本步骤:
```python
# 导入所需库
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
# 加载鸢尾花数据集
iris = datasets.load_iris()
X = iris.data # 特征
y = iris.target # 类别标签
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 创建KNN分类器
knn = KNeighborsClassifier(n_neighbors=5) # 通常选择合适的k值
# 训练模型
knn.fit(X_train, y_train)
# 预测
y_pred = knn.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"KNN分类器的预测精度为: {accuracy*100}%")
#
相关问题
python使用KNN算法实现鸢尾花数据的分类
好的,我可以为您提供一个简单的Python代码示例,演示如何使用KNN算法对鸢尾花数据进行分类。首先,我们需要导入必要的库,包括NumPy和sklearn:
```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
```
接下来,我们将加载鸢尾花数据集,并将其拆分为训练集和测试集:
```python
# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target
# 将数据集拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
然后,我们可以使用KNeighborsClassifier类来训练KNN模型:
```python
# 训练KNN模型
k = 5
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
```
最后,我们可以使用训练好的模型对测试集进行预测,并计算模型的准确率:
```python
# 对测试集进行预测并计算准确率
y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
完整的代码如下:
```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target
# 将数据集拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练KNN模型
k = 5
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
# 对测试集进行预测并计算准确率
y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
希望这个示例对您有所帮助!
用KNN算法对鸢尾花数据集分类的代码
这是一个用Python实现的KNN算法对鸢尾花数据集进行分类的示例代码:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
# 创建KNN分类器,并用训练集拟合模型
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
# 在测试集上进行预测,并计算准确率
y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
```
这段代码使用了sklearn库中的KNN分类器,并将其应用于鸢尾花数据集上。首先,数据集被加载并划分为训练集和测试集。然后,使用训练集对KNN分类器进行了拟合。最后,在测试集上进行预测,并计算预测准确率。