准确率是0.67，下一篇博客将去掉偏差大的特征。 import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns df = pd.read_csv('train.csv') df1=pd.read_csv('test.csv') df=df.drop(['ID'],axis=1) df=df.to_numpy() feature=df[:,:-1] label=df[:,-1] from sklearn.model_selection import train_test_split tfeature,ttest,tlabel,testlabel=train_test_split(feature,label,test_size=0.2) from sklearn import tree from sklearn.metrics import accuracy_score from sklearn.model_selection import KFold kf=KFold(n_splits=5,shuffle=False) from sklearn import svm from sklearn.model_selection import cross_val_score for k in range(10): sum=0 sum1=0 i=0 for train_index,test_index in kf.split(df): i=i+1 tfeature=df[train_index,:-1] label=df[train_index,-1] clf=tree.DecisionTreeClassifier(criterion='gini',random_state=0,max_depth=k+1) clf.fit(tfeature,tlabel) l=clf.predict(tfeature) ttest=df[test_index,:-1] testlabel=df[test_index,-1] l1=clf.predict(ttest) pr=accuracy_score(tlabel, l) pr1=accuracy_score(testlabel, l1) sum=sum+pr sum1=sum1+pr clf1=tree.DecisionTreeClassifier(criterion='gini',random_state=0,max_depth=k+1) scores = cross_val_score(clf, df[:,:-1], df[:,-1], cv=5) print(k,sum/i,sum1/i,scores.mean()) clf=tree.DecisionTreeClassifier(criterion='gini',random_state=0,max_depth=11) clf.fit(df[:,:-1],df[:,-1]) df1=df1.drop(['ID'],axis=1) df1=df1.to_numpy() y=clf.predict(df1[:,:]) out=pd.DataFrame(y) out.columns = ['CLASS'] w=[] for k in range(out.shape[0]): w.append(k+210) out['ID']=np.reshape(w,(-1,1)) out[['ID','CLASS']].to_csv('out.csv',index=False)
标签：index,sklearn,baseline,df,clf,test,import,直接,决策树 From： https://www.cnblogs.com/hahaah/p/16968504.html