在Python中如何通过KNN算法实现对鸢尾花数据集的分类,并通过调整参数达到高准确率?请结合《基于Python的KNN鸢尾花分类实践教程》提供具体的操作步骤。
时间: 2024-10-31 18:09:53 浏览: 45
为了帮助你更深入地理解KNN算法在Python中的应用以及如何优化模型以获得高准确率,推荐参阅《基于Python的KNN鸢尾花分类实践教程》。这份教程将带你一步步实践KNN算法,并对参数调整进行详细讲解。
参考资源链接:[基于Python的KNN鸢尾花分类实践教程](https://wenku.csdn.net/doc/ty5zex8vcn?spm=1055.2569.3001.10343)
首先,确保安装了必要的Python库,如NumPy、pandas和scikit-learn。接下来,载入鸢尾花数据集,并使用scikit-learn库中的KNeighborsClassifier类来构建KNN模型。以下是实现KNN分类和优化的步骤:
1. 导入所需的库:
```python
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
```
2. 加载鸢尾花数据集,并将其分为特征和标签:
```python
iris = datasets.load_iris()
X = iris.data
y = iris.target
```
3. 将数据集分为训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
4. 初始化KNN分类器,并选择合适的K值。通常,通过交叉验证来确定最优的K值:
```python
k_values = list(range(1, 11))
accuracies = []
for k in k_values:
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracies.append(accuracy_score(y_test, y_pred))
# 找到最佳的K值
best_k = k_values[np.argmax(accuracies)]
```
5. 使用选定的K值重新训练模型,并进行预测:
```python
knn = KNeighborsClassifier(n_neighbors=best_k)
knn.fit(X_train, y_train)
predictions = knn.predict(X_test)
```
6. 计算最终模型的准确率,并通过混淆矩阵等方法评估模型性能:
```python
final_accuracy = accuracy_score(y_test, predictions)
print(f
参考资源链接:[基于Python的KNN鸢尾花分类实践教程](https://wenku.csdn.net/doc/ty5zex8vcn?spm=1055.2569.3001.10343)
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