gbdt和xgboost的调参模板

#Import libraries:
import pandas as pd
import numpy as np

import xgboost as xgb #XGBOOST
from xgboost.sklearn import XGBClassifier

from sklearn.ensemble import GradientBoostingClassifier  #GBDT 

#交叉验证和寻找最优参数的库
from sklearn import cross_validation, metrics   
from sklearn.grid_search import GridSearchCV  


import matplotlib.pylab as plt
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 4


#读数据
train = pd.read_csv('data.csv')
#目标和数据ID
target = 'Disbursed'
IDcol = 'ID'



#GBDT模型训练和特征重要性提取，以及利用交叉验证寻找最优的 树的数量 的参数
#参数解释 ：alg 模型  dtrain训练数据 predictors训练数据里面的特征维度  performCV 是否用交叉验证（第一次训练设为True，
#找到最优的参数以后，后面改为false，在传入模型的时候设置树的个数）
#cv_folds 交叉验证分组数  printFeatureImportance 输出特征重要性
def modelfitGBDT(alg, dtrain, predictors, performCV=True, printFeatureImportance=True, cv_folds=5):
    #Fit the algorithm on the data
    alg.fit(dtrain[predictors], dtrain['Disbursed'])
        
    #Predict training set:
    dtrain_predictions = alg.predict(dtrain[predictors])
    dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]
    
    #Perform cross-validation:
    if performCV:
        cv_score = cross_validation.cross_val_score(alg, dtrain[predictors], dtrain['Disbursed'], cv=cv_folds, scoring='roc_auc')
    
    #Print model report:
    print "\nModel Report"
    print "Accuracy : %.4g" % metrics.accuracy_score(dtrain['Disbursed'].values, dtrain_predictions)
    print "AUC Score (Train): %f" % metrics.roc_auc_score(dtrain['Disbursed'], dtrain_predprob)
    
    if performCV:
        print "CV Score : Mean - %.7g | Std - %.7g | Min - %.7g | Max - %.7g" % (np.mean(cv_score),np.std(cv_score),np.min(cv_score),np.max(cv_score))
        
    #Print Feature Importance:
    if printFeatureImportance:
        feat_imp = pd.Series(alg.feature_importances_, predictors).sort_values(ascending=False)
        feat_imp.plot(kind='bar', title='Feature Importances')
        plt.ylabel('Feature Importance Score')

#随便试着训练一下
predict