当前位置:   article > 正文

对红酒数据集,分别采用决策树算法和随机森林算法进行分类。

对红酒数据集,分别采用决策树算法和随机森林算法进行分类。

1.导入所需要的包

  1. from sklearn.tree import DecisionTreeClassifier
  2. from sklearn.ensemble import RandomForestClassifier
  3. from sklearn.datasets import load_wine
  4. from sklearn.model_selection import train_test_split

2.导入数据,并且对随机森林和决策数进行对比 

  1. x_train,x_test,y_train,y_test=train_test_split(wine.data,wine.target,test_size=0.3)
  2. clf=DecisionTreeClassifier(random_state=0)
  3. rfc=RandomForestClassifier(random_state=0)
  4. clf=clf.fit(x_train,y_train)
  5. rfc=rfc.fit(x_train,y_train)
  6. score_c=clf.score(x_test,y_test)
  7. score_r=rfc.score(x_test,y_test)
  8. print(score_c,score_r)

运行结果:

0.8703703703703703            0.9259259259259259

3.数据可视化 

  1. from sklearn.tree import DecisionTreeClassifier
  2. from sklearn.ensemble import RandomForestClassifier
  3. from sklearn.datasets import load_wine
  4. from sklearn.model_selection import cross_val_score
  5. import matplotlib.pyplot as plt
  6. %matplotlib inline
  7. wine=load_wine()
  8. rfc=RandomForestClassifier(n_estimators=25)
  9. rfc_s=cross_val_score(rfc,wine.data,wine.target,cv=10)
  10. clf=DecisionTreeClassifier()
  11. clf_s=cross_val_score(clf,wine.data,wine.target,cv=10)
  12. plt.plot(range(1,11),rfc_s,label='RandomForest')
  13. plt.plot(range(1,11),clf_s,label='DecisionTree')
  14. plt.legend()
  15. plt.show()

运行结果: 

 

 

 

 

声明:本文内容由网友自发贡献,不代表【wpsshop博客】立场,版权归原作者所有,本站不承担相应法律责任。如您发现有侵权的内容,请联系我们。转载请注明出处:https://www.wpsshop.cn/w/小惠珠哦/article/detail/748863
推荐阅读
相关标签
  

闽ICP备14008679号