基于鸢尾花数据集的sklearn KNN分类.zip

import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.metrics import classification_report,confusion_matrix # 混淆矩阵和准确率，召回率，F1评分 from sklearn.model_selection import train_test_split # 分割 from sklearn.preprocessing import StandardScaler # 梯度下降法收敛数据 from sklearn.neighbors import KNeighborsClassifier # knn分类器 # 装载数据集 url="iris.csv" names =['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class'] dataset=pd.read_csv(url, names=names) # pandas的纵向切割，将数据集分割成属性（X）和标签（Y） X=dataset.iloc[:,:-1].values y=dataset.iloc[:,4].values # 划分数据集和测试集,70%训练，30%测试 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30) # 梯度下降法缩放数据 scaler=StandardScaler() scaler.fit(X_train) X_train=scaler.transform(X_train) X_test=scaler.transform(X_test) # 训练模型 classifier=KNeighborsClassifier(n_neighbors=6) # k取6效果感觉比书上取5效果更好 classifier.fit(X_train,y_train) # 在测试集上进行预测 y_pred=classifier.predict(X_test) # 混淆矩阵 准确率 召回率 F1评分 评估模型优劣 print(confusion_matrix(y_test,y_pred)) print(classification_report(y_test,y_pred)) # 计算k取1到40模型的分类误差率 error=[] for i in range(1,40): knn=KNeighborsClassifier(n_neighbors=i) knn.fit(X_train,y_train) pred_i=knn.predict(X_test) error.append(np.mean(pred_i != y_test)) # 绘图 plt.figure(figsize=(12,6)) plt.plot(range(1,40),error,color='red',linestyle='dashed',marker='o',markerfacecolor='blue',markersize=10) plt.title('Error Rate K Value') plt.xlabel('K Value') plt.ylabel('Mean Error') plt.show()