python鸢尾花数据进行分析knn算法

对于鸢尾花数据集进行分析，KNN算法是一个常用的分类算法。下面是使用Python进行实现的步骤：

1. 导入所需的库和数据集

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

iris = load_iris()
X, y = iris.data, iris.target

2. 划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

3. 初始化KNN算法模型

knn = KNeighborsClassifier(n_neighbors=3)

4. 使用训练集来训练模型

knn.fit(X_train, y_train)

5. 使用测试集来测试模型的准确性

y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

完整代码如下：

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

iris = load_iris()
X, y = iris.data, iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

knn = KNeighborsClassifier(n_neighbors=3)

knn.fit(X_train, y_train)

y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

运行代码后，输出的准确率就是我们所求得的模型准确性。