这个问题非常奇怪，因为那段代码在其他数据集上运行得很好。

完整代码如下：

import numpy as npimport pandas as pdimport xgboost as xgbfrom sklearn.cross_validation import train_test_split# # 分割学习集X_fit, X_eval, y_fit, y_eval= train_test_split(    train, target, test_size=0.2, random_state=1)clf = xgb.XGBClassifier(missing=np.nan, max_depth=6,                         n_estimators=5, learning_rate=0.15,                         subsample=1, colsample_bytree=0.9, seed=1400)# 拟合clf.fit(X_fit, y_fit, early_stopping_rounds=50, eval_metric="logloss", eval_set=[(X_eval, y_eval)])#print y_predy_pred= clf.predict_proba(test)[:,1]

最后一行引发了以下错误（提供了完整输出）：

将训练直到验证_0错误在50轮内没有减少。[0] validation_0-logloss:0.554366[1] validation_0-logloss:0.451454[2] validation_0-logloss:0.372142[3] validation_0-logloss:0.309450[4] validation_0-logloss:0.259002Traceback (most recent call last):  File "../src/script.py", line 57, in     y_pred= clf.predict_proba(test)[:,1]  File "/opt/conda/lib/python3.4/site-packages/xgboost-0.4-py3.4.egg/xgboost/sklearn.py", line 435, in predict_proba    test_dmatrix = DMatrix(data, missing=self.missing)  File "/opt/conda/lib/python3.4/site-packages/xgboost-0.4-py3.4.egg/xgboost/core.py", line 220, in __init__    feature_types)  File "/opt/conda/lib/python3.4/site-packages/xgboost-0.4-py3.4.egg/xgboost/core.py", line 147, in _maybe_pandas_data    raise ValueError('DataFrame.dtypes for data must be int, float or bool')ValueError: DataFrame.dtypes for data must be int, float or boolException ignored in: >Traceback (most recent call last):  File "/opt/conda/lib/python3.4/site-packages/xgboost-0.4-py3.4.egg/xgboost/core.py", line 289, in __del__    _check_call(_LIB.XGDMatrixFree(self.handle))AttributeError: 'DMatrix' object has no attribute 'handle'

这里出了什么问题？我不知道如何修复它

更新1：实际上这是kaggle的问题： https://www.kaggle.com/insaff/bnp-paribas-cardif-claims-management/xgboost

回答：

这里的问题与初始数据有关：一些值是浮点数或整数，而另一些是对象。这就是为什么我们需要转换它们：

from sklearn import preprocessing for f in train.columns:     if train[f].dtype=='object':         lbl = preprocessing.LabelEncoder()         lbl.fit(list(train[f].values))         train[f] = lbl.transform(list(train[f].values))for f in test.columns:     if test[f].dtype=='object':         lbl = preprocessing.LabelEncoder()         lbl.fit(list(test[f].values))         test[f] = lbl.transform(list(test[f].values))train.fillna((-999), inplace=True) test.fillna((-999), inplace=True)train=np.array(train) test=np.array(test) train = train.astype(float) test = test.astype(float)