模型在测试集的准确率为0.74提升了一些说明根据相关系数取模型是不错的选择。 import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns df = pd.read_csv('train.csv') df=df.drop(['ID'],axis=1) df=df.to_numpy() feature=np.abs(np.fft.fft(df[:,:-1])) feature=np.concatenate((feature,np.reshape(df[:,-1],(-1,1))),axis=1) train=pd.DataFrame(feature) heat=train.corr() fe=heat.index[abs(heat[240])>0.3] train=train.to_numpy() train=train[:,fe] from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn import tree from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold kf=KFold(n_splits=5,shuffle=False) for k in range(30): sum=0 sum1=0 i=0 for train_index,test_index in kf.split(train): i=i+1 tfeature=train[train_index,:-1] label=train[train_index,-1] clf=tree.DecisionTreeClassifier(criterion='entropy',random_state=0,max_depth=k+1) clf.fit(tfeature,label) l=clf.predict(tfeature) ttest=train[test_index,:-1] testlabel=train[test_index,-1] l1=clf.predict(ttest) pr=accuracy_score(label, l) pr1=accuracy_score(testlabel, l1) sum=sum+pr sum1=sum1+pr1 clf1=tree.DecisionTreeClassifier(criterion='entropy',random_state=0,max_depth=k+1) scores = cross_val_score(clf1, train[:,:-1], train[:,-1], cv=5) print(k,sum/i,sum1/i,scores.mean()) clf1=tree.DecisionTreeClassifier(criterion='entropy',random_state=0,max_depth=4+1) clf1.fit(train[:,:-1],train[:,-1]) df1 = pd.read_csv('test.csv') df1=df1.drop(['ID'],axis=1) df1=df1.to_numpy() feature=np.abs(np.fft.fft(df1[:,:])) feature=feature[:,fe[:-1]] out=clf1.predict(feature) out=pd.DataFrame(out) 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('out3.csv',index=False)
标签：index,baseline,0.3,feature,train,np,import,out,决策树 From： https://www.cnblogs.com/hahaah/p/16968772.html