塞了一堆特征模型的准确率并没提高多少

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import torch

import torch.fft as fft

from sklearn import tree

df = pd.read_csv('train.csv')

df=df.drop(['ID'],axis=1)

nmp=df.to_numpy()

feature=nmp[:,:-1]

df=pd.DataFrame(feature)

sk=np.reshape(np.array(df.skew(axis=1)),(-1,1))

Q25=np.reshape(np.array(df.quantile(q=0.25,axis=1)),(-1,1))

Q75=np.reshape(np.array(df.quantile(q=0.75,axis=1)),(-1,1))

dmax=np.reshape(np.array(df.diff(1).max(axis=1)),(-1,1))

dmin=np.reshape(np.array(df.diff(1).min(axis=1)),(-1,1))

range_diff1=dmax-dmin

dvar=np.reshape(np.array(df.diff(1).var(axis=1)),(-1,1))

dstd=np.reshape(np.array(df.diff(1).std(axis=1)),(-1,1))

dmean=np.reshape(np.array(df.diff(1).mean(axis=1)),(-1,1))

dmedia=np.reshape(np.array(df.diff(1).median(axis=1)),(-1,1))

dsk=np.reshape(np.array(df.diff(1).skew(axis=1)),(-1,1))

dQ25=np.reshape(np.array(df.diff(1).quantile(q=0.25,axis=1)),(-1,1))

dQ75=np.reshape(np.array(df.diff(1).quantile(q=0.75,axis=1)),(-1,1))

dk=np.reshape(np.array(df.diff(1).kurtosis(axis=1)),(-1,1))

feature=nmp[:,:-1]

label=nmp[:,-1]#（210，240）

min=np.reshape(nmp[:,:-1].min(-1),(-1,1))

max=np.reshape(nmp[:,:-1].max(-1),(-1,1))

ra=min-max

var=np.reshape(np.var(nmp[:,:-1],axis=1),(-1,1))

std=np.reshape(np.std(nmp[:,:-1],axis=1),(-1,1))

mean=np.reshape(np.mean(nmp[:,:-1],axis=1),(-1,1))

media=np.reshape(np.median(nmp[:,:-1],axis=1),(-1,1))

feature1=torch.fft.fft(torch.Tensor(feature))

feature1=torch.abs(feature1)/240*2

feature1=feature1.detach().numpy()

df=pd.DataFrame(feature1)

sk3=np.reshape(np.array(df.skew(axis=1)),(-1,1))

Q253=np.reshape(np.array(df.quantile(q=0.25,axis=1)),(-1,1))

Q753=np.reshape(np.array(df.quantile(q=0.75,axis=1)),(-1,1))

dmax3=np.reshape(np.array(df.diff(1).max(axis=1)),(-1,1))

dmin3=np.reshape(np.array(df.diff(1).min(axis=1)),(-1,1))

range_diff3=dmax-dmin

dvar3=np.reshape(np.array(df.diff(1).var(axis=1)),(-1,1))

dstd3=np.reshape(np.array(df.diff(1).std(axis=1)),(-1,1))

dmean3=np.reshape(np.array(df.diff(1).mean(axis=1)),(-1,1))

dmedia3=np.reshape(np.array(df.diff(1).median(axis=1)),(-1,1))

dsk3=np.reshape(np.array(df.diff(1).skew(axis=1)),(-1,1))

dQ253=np.reshape(np.array(df.diff(1).quantile(q=0.25,axis=1)),(-1,1))

dQ753=np.reshape(np.array(df.diff(1).quantile(q=0.75,axis=1)),(-1,1))

dk3=np.reshape(np.array(df.diff(1).kurtosis(axis=1)),(-1,1))

min1=np.reshape(feature1.min(-1),(-1,1))

max1=np.reshape(feature1.max(-1),(-1,1))

var1=np.reshape(np.var(feature1,axis=1),(-1,1))

std1=np.reshape(np.std(feature1,axis=1),(-1,1))

mean1=np.reshape(np.mean(feature1,axis=1),(-1,1))

media1=np.reshape(np.median(feature1,axis=1),(-1,1))

import scipy

kur=np.reshape(scipy.stats.kurtosis(feature,axis=1),(-1,1))

ne=np.concatenate((sk3,Q253,Q753,dmax3,dmin3,dk3,range_diff3,dvar3,dstd3,dmean3,dsk3,dQ253,dQ753,dmedia3,dk,dsk,dQ25,dQ75,dmean,dmedia,dvar,dstd,range_diff1,dmax,dmin,Q25,Q75,sk,kur,feature,ra,min,max,var,std,mean,media,feature1,min1,max1,var1,std1,mean1,media1),axis=1)

from sklearn.model_selection import cross_val_score

from sklearn import svm

import matplotlib.pyplot as plt

from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import GradientBoostingClassifier

for i in range(300):

    for j in range (20):

    #clf = tree.DecisionTreeClassifier(criterion='gini',random_state=0,max_depth=i)

        #clf=RandomForestClassifier(criterion='gini',n_estimators=10*i+1,max_depth=j+1)

        clf=GradientBoostingClassifier(criterion='mse',n_estimators=5*i+1,max_depth=j+1)

        scores = cross_val_score(clf,feature,label,cv=10)

        print(i,j,scores.mean())

        if scores.mean()>=0.9:

            print('123')