Question

我正在玩XGBoost的新数据集。以下是我的代码：

import xgboost as xgb
import pandas as pd
import numpy as np

train = pd.read_csv("train_users_processed_onehot.csv")
labels = train["Buy"].map({"Y":1, "N":0})

features = train.drop("Buy", axis=1)
data_dmat = xgb.DMatrix(data=features, label=labels)

params={"max_depth":5, "min_child_weight":2, "eta": 0.1, "subsamples":0.9, "colsample_bytree":0.8, "objective" : "binary:logistic", "eval_metric": "logloss", "seed": 2333}
rounds = 6000

result = xgb.cv(params=params, dtrain=data_dmat, num_boost_round=rounds, early_stopping_rounds=50, as_pandas=True, seed=2333)
print result

结果是（省略中间结果）：

         test-logloss-mean  test-logloss-std  train-logloss-mean  
0             0.683354          0.000058            0.683206  
165           0.622318          0.000661            0.607680

但是当我尝试使用GridSearchCV进行参数调整时，我发现结果完全不同。更具体地说，这是我的代码：

import xgboost as xgb
from sklearn.model_selection import GridSearchCV
from xgboost.sklearn import XGBClassifier
import numpy as np
import pandas as pd

train_dataframe = pd.read_csv("train_users_processed_onehot.csv")
train_labels = train_dataframe["Buy"].map({"Y":1, "N":0})
train_features = train_dataframe.drop("Buy", axis=1)

params = {"max_depth": [5], "min_child_weight": [2]}

estimator = XGBClassifier(learning_rate=0.1, n_estimators=170, max_depth=2, min_child_weight=4, objective="binary:logistic", subsample=0.9, colsample_bytree=0.8, seed=2333)

gsearch1 = GridSearchCV(estimator, param_grid=params, n_jobs=4, iid=False, verbose=1, scoring="neg_log_loss")
gsearch1.fit(train_features.values, train_labels.values)

print pd.DataFrame(gsearch1.cv_results_)
print gsearch1.best_params_
print -gsearch1.best_score_

我得到了：

   mean_fit_time  mean_score_time  mean_test_score  mean_train_score  
0       87.71497         0.209772        -3.134132         -0.567306

很明显3.134132与0.622318非常不同。这是什么原因？

谢谢！

Answer 1

您将不同的参数传递给两者：

max_depth：5 vs 2
eta：0.1 vs 0.3（默认）
min_child_weight：2 vs 4

您传递给sklearn的参数更加保守（您不太可能过度拟合模型），因此算法不会尝试将模型与数据相匹配。反过来，第二个你得分较低 - 确切地说应该是什么。

XGBoost和Sklearn中的日志丢失是否相同？

1 个答案: