变化型网站,网上国网下载,企业风险查询平台,做网站最便宜通过导入必要的scikit-learn导入必要的库#xff0c;加载给定的数据#xff0c;划分测试集和训练集之后训练预测和评估即可
具体代码如下#xff1a;
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
f…通过导入必要的scikit-learn导入必要的库加载给定的数据划分测试集和训练集之后训练预测和评估即可
具体代码如下
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix# 加载鸢尾花数据集
iris load_iris()
X iris.data
y iris.target# 划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_size0.3, random_state42)# 标准化数据
scaler StandardScaler()
X_train scaler.fit_transform(X_train)
X_test scaler.transform(X_test)# 创建KNN分类器并训练模型
knn KNeighborsClassifier(n_neighbors3)
knn.fit(X_train, y_train)# 使用测试集进行预测
y_pred knn.predict(X_test)# 输出结果
print(Accuracy:, accuracy_score(y_test, y_pred))
print(Classification Report:)
print(classification_report(y_test, y_pred, target_namesiris.target_names))
print(Confusion Matrix:)
print(confusion_matrix(y_test, y_pred))
运行结果
