xgboost调优

最新推荐文章于 2025-06-01 09:04:39 发布

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最新推荐文章于 2025-06-01 09:04:39 发布 · 539 阅读

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#xgboost调优

1.xgboost参数：
（1）XGBClassifier
class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective=‘binary:logistic’,
booster=‘gbtree’, n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1,
colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)

（2）xgb
参数对比

（3）用XGBClassifier，结合xgb.cv进行调优，首先调整树的数量

import pandas as pd
import numpy as np
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn import cross_validation, metrics   #Additional     scklearn functions
from sklearn.grid_search import GridSearchCV   #Perforing grid search
 
import matplotlib.pylab as plt
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 4

train = pd.read_csv('train.tx