調參數是一件很頭疼的事情，今天學習到一個較為簡便的跑循環交叉驗證的方法，雖然不是最好的，如今網上有很多調參的技巧，目前覺得實現簡單的，以後了解更多了再更新。

import numpy as np
from pandas import read_csv
import pandas as pd
import sys  
import importlib
from sklearn.neighbors import KNeighborsClassifier     
from sklearn.ensemble import GradientBoostingClassifier    
from sklearn import
 
 svm
from sklearn import cross_validation
from sklearn.metrics import hamming_loss
from sklearn import metrics    
importlib.reload(sys)
from sklearn.linear_model import LogisticRegression 
from imblearn.combine import SMOTEENN
from sklearn.tree import DecisionTreeClassifier 
from sklearn.ensemble import
 
 RandomForestClassifier  #92%
from sklearn import tree
from xgboost.sklearn import XGBClassifier
from sklearn.linear_model import SGDClassifier
from sklearn import neighbors
from sklearn.naive_bayes import BernoulliNB
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.metrics import
 
 confusion_matrix
from numpy import mat
from sklearn.cross_validation import cross_val_score


‘‘‘分類0-1‘‘‘
root1="D:/ProgramData/station3/get_10.csv"
root2="D:/ProgramData/station3/get_more.csv"
root3="D:/ProgramData/station3/get_more9_10.csv"#現已刪除

data1 = read_csv(root3) #數據轉化為數組
#print(data1)
data1=data1.values
#print(root3)


‘‘‘設置調參的參數範圍‘‘‘
k_range = range(10,30)
k_scores = []
for k in k_range:
    print(k)
    clf = XGBClassifier(learning_rate= 0.28, min_child_weight=0.7, max_depth=21,
                        gamma=0.2, n_estimators = k ,seed=1000)

    X_Train = data1[:,:-1]
    Y_Train = data1[:,-1]

    X, y = SMOTEENN().fit_sample(X_Train, Y_Train)
    
    ‘‘‘交叉驗證，循環跑，cv是每次循的次數‘‘‘
    scores = cross_val_score(clf, X, y, cv=10, scoring=‘accuracy‘)
    ‘‘‘得到平均值‘‘‘
    k_scores.append(scores.mean())

print(k_scores)

‘‘‘畫出圖像‘‘‘
plt.plot(k_range, k_scores)
plt.xlabel("Value of K for KNN")
plt.ylabel("Cross validated accuracy")
plt.show()

輸出：

10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
[0.97412964956075354, 0.9727391061798798, 0.97013721043132806, 0.97194859003086298, 0.97294995933403183, 0.97231558127609719, 0.96986246148807409, 0.97110881317341868, 0.97266934815981898, 0.97129916102078995, 0.97418346359522834, 0.97193130399012762, 0.97399918501338656, 0.96896580483736439, 0.9699006181068961, 0.96718751547141646, 0.96806405658951156, 0.97446711004726705, 0.97459883656499302, 0.97087357015888409]

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分類預測，交叉驗證調超參數