python进行KNN算法分析实战(鸢尾花数据集)

1、python进行KNN算法分析实战（鸢尾花数据集）算法分析实战（鸢尾花数据集）目录代码效果图Run:knnffshiyanC:Users72796AppDataLocalProgpamsPythonPython38python.exeD:/pythoi=50Sepal.Length5.1Sepaljidth3.5Petal-Length1.4PetaljidthSpecies0.2setosa14.93.01.40.2setosa24.73.21.30.2setosa34.63.11.50.2setosa45.03.61.40.2setosa8.01.2.3.45.6.7.聲尾花种类混淆邸a氷

2、:Figure1莺尾花种类热力图2500o25000251setosa1versicolorRealLablevirginicaxl丄C=叭o252015-10-5-0Run:knn#shiyant莺尾花种类混淆矩阵Speciessetosaversicolorvirginicarow_0setosa25001versicolor6250avirginica6D25rw显示各类预测准确率1*1precisionrecallfl-scores叩portIsetosaL00UOL0D25versicolorL00uoL0D25virginicaL00uoL0D25提示：以下是本篇文章正文内容，下厚

3、案例可供参考L0D75导入需要的包。世L00L00L0D75要是报错的话可以在占伸rm安装包,要是不行就在命令窗口输1入pipinstall+包名importmatplotlib.pyplotaspitfromsklearnimportneighborsimportnumpyasnpimportpandasaspdimportseabornassnsfromsklearnimportmodel_selectionfromsklearnimportmetrics1导入数据导入数据并查看前5行代码df1=pd.read_csv(rD:pythoniris.csv)print(df1.head输出前

4、五行predictors=df1.columns:-1Sipal_LEngthSepal_WidthPetal_LengthPetal_WidthSpecies05.13*5IM0.2setcsa193+0IM0.2setcsa24J3*21.30.2setcsa33+l1.50.2setcsa2建立训练集和测试集7L1zr小4严rc代码如下：x_train,x_test,y_train,y_test=model_selection.train_test_split(df1predictors,df1.Species,test_size=0.5,random_state=1234)print(

5、np.ceil(npog2(df1.shapeO)3设置K值#设置待测试的不同K值K=np.arange(1,np.ceil(np.log2(df1.shape0)print(np.arange(1,np.ceil(np.log2(df1.shape0)#设置空列表，用于储存平均准确率accuracy=4.十重交叉验证K值使用十重交叉验证K值，并做出最适合K值的折线图#使用十重交叉验证的方法forkinK:cv_result=model_selection.cross_val_score(neighbors.KNeighborsClassifier(n_neighbors=int(k),wei

6、ghts=distance),x_train,y_train,cv=10,scoring=accuracy)accuracy.append(cv_result.mean()#从个平均准确率中挑选出最大值做对应的目标arg_max=np.array(accuracy).argmax()#中文负号正常显示plt.rcParamsfont.sans-serif=SimHeiplt.rcParamsaxes.unicode_minus=False#绘制不同k值与准确率之间的折线图plt.plot(K,accuracy)plt.scatter(K,accuracy)plt.text(Karg_max,a

7、ccuracyarg_max,最佳K值为%s%int(Karg_max)plt.show()进行模型拟合knn_class=neighbors.KNeighborsClassifier(n_neighbors=7,weights=distance)#模型拟合knn_class.fit(x_train,y_train)#模型在测试集上的预测predict=knn_class.predict(x_test)数.据可视化输出#构建混淆矩阵cm=pd.crosstab(predict,y_test)print(f鸢尾花种类混淆矩P$ncm)#热力图输出cm=pd.DataFrame(cm,column

8、s=setosa,versicolor,virginica,index=setosa,versicolor,virginica)sns.heatmap(cm,annot=True,cmap=GnBu)plt.xlabel(RealLable)plt.ylabel(PredictLable)plt.title(鸢尾花种类热力图)plt.show()#显示各类预测准确率b=metrics.classification_report(y_test,predict)print(f显示各类预测准确nb)1reU呵莺尾花种类热力图25-OESa)A切一qm-l4C一PTd25-5Run:knn#shiyanSpeciessetosaversicolorvirginicarow_0setosa250QversicolorQ25Qvi