XGBoost XGBClassifier在Python中默认值

我试图使用XGBoosts分类器来分类一些二进制数据.当我做最简单的事情并只使用默认值(如下)

clf = xgb.XGBClassifier()
metLearn=CalibratedClassifierCV(clf, method='isotonic', cv=2)
metLearn.fit(train, trainTarget)
testPredictions = metLearn.predict(test)

我得到了相当不错的分类结果.

我的下一步是尝试调整我的参数.从参数指南猜测... https://github.com/dmlc/xgboost/blob/master/doc/parameter.md 我想从默认开始并从那里工作......

# setup parameters for xgboost
param = {}
param['booster'] = 'gbtree'
param['objective'] = 'binary:logistic'
param["eval_metric"] = "error"
param['eta'] = 0.3
param['gamma'] = 0
param['max_depth'] = 6
param['min_child_weight']=1
param['max_delta_step'] = 0
param['subsample']= 1
param['colsample_bytree']=1
param['silent'] = 1
param['seed'] = 0
param['base_score'] = 0.5

clf = xgb.XGBClassifier(params)
metLearn=CalibratedClassifierCV(clf, method='isotonic', cv=2)
metLearn.fit(train, trainTarget)
testPredictions = metLearn.predict(test)

结果是预测的一切都是条件而不是其他条件.

奇怪的是,如果我设置

params={}

我期望给我相同的默认值,因为没有提供任何参数,我得到同样的事情发生

那么有谁知道XGBclassifier的默认值是什么？这样我才能开始调音？

这不是你在xgboost中设置参数的方式.您可能希望将param网格传递到训练函数中,例如xgboost train或sklearn GridSearchCV,或者您希望使用XGBClassifier的set_params方法.另外需要注意的是,如果你使用xgboost的包装器来sklearn(即:XGBClassifier()或者XGBRegressor()类),那么使用的参数名称与sklearn自己的GBM类中使用的名称相同(例如:eta - > learning_rate).我没有看到隐藏sklearn包装器的确切文档的位置,但这些类的代码在这里:https://github.com/dmlc/xgboost/blob/master/python-package/xgboost/sklearn.py

这里您将参考如何直接设置模型对象参数.

>>> grid = {'max_depth':10}
>>> 
>>> clf = XGBClassifier()
>>> clf.max_depth
3
>>> clf.set_params(**grid)
XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=10,
       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=0, silent=True, subsample=1)
>>> clf.max_depth
10

编辑:我想你可以在模型创建上设置参数,这样做并不是非常典型,因为大多数人都会通过某种方式进行网格搜索.但是,如果您这样做,则需要将它们列为完整参数或使用**kwargs.例如:

>>> XGBClassifier(max_depth=10)
XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=10,
       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=0, silent=True, subsample=1)
>>> XGBClassifier(**grid)
XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=10,
       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=0, silent=True, subsample=1)

使用字典作为输入而不使用**kwargs会将该参数设置为字面上的字典:

>>> XGBClassifier(grid)
XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,
       gamma=0, learning_rate=0.1, max_delta_step=0,
       max_depth={'max_depth': 10}, min_child_weight=1, missing=None,
       n_estimators=100, nthread=-1, objective='binary:logistic',
       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=0, silent=True,
       subsample=1)

XGBClassifier的默认值为:

MAX_DEPTH = 3

learning_rate = 0.1

n_estimators = 100

沉默=真

客观="二进制:物流"

升压= 'gbtree'

n_jobs = 1

nthread =无

伽马= 0

min_child_weight = 1

max_delta_step = 0

子样本= 1

colsample_bytree = 1

colsample_bylevel = 1

reg_alpha = 0

reg_lambda = 1

scale_pos_weight = 1

base_score = 0.5

random_state = 0

种子=无

缺少=无

使用类默认值链接到XGBClassifier文档:https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier