sklearn knn python代码
时间: 2023-08-30 18:09:21 浏览: 88
以下是使用sklearn库实现KNN算法的Python代码示例:
```python
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
# 导入数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target
# 切分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
# 创建KNN分类器对象
knn = KNeighborsClassifier(n_neighbors=3)
# 拟合训练集数据
knn.fit(X_train, y_train)
# 预测测试集数据
y_pred = knn.predict(X_test)
# 打印预测结果
print("预测结果:", y_pred)
```
请注意,这只是一个简单的示例代码,实际使用时可能需要根据具体情况进行适当的调整和优化。
相关问题
sklearnknn分类模型代码
可以使用下面的代码来实现使用sklearn库中的KNN分类模型:
```python
# 导入所需依赖库
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
# 切分数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 创建KNN分类模型
knn = KNeighborsClassifier(n_neighbors=5)
# 在训练集上训练模型
knn.fit(X_train, y_train)
# 在测试集上进行预测
y_pred = knn.predict(X_test)
# 计算预测准确率
accuracy = accuracy_score(y_test, y_pred)
# 打印预测准确率
print("KNN分类模型的预测准确率为: ", accuracy)
```
在上述代码中,首先导入了所需的依赖库,然后加载了鸢尾花数据集。接下来,将数据集划分为训练集和测试集,然后创建了一个KNN分类模型并在训练集上进行训练。最后,使用训练好的模型对测试集进行预测,计算预测准确率并输出结果。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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[ .reference_list ]
knn算法python代码sklearn
以下是使用 scikit-learn 库实现 KNN 算法的 Python 代码示例:
```python
from sklearn.neighbors import KNeighborsClassifier
# 创建 KNN 分类器
knn = KNeighborsClassifier(n_neighbors=3)
# 训练模型
knn.fit(X_train, y_train)
# 预测
y_pred = knn.predict(X_test)
# 评估模型
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))
```
其中 `X_train` 和 `y_train` 是训练数据的特征和标签, `X_test` 和 `y_test` 是测试数据的特征和标签, `n_neighbors` 是设定的近邻数量.
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