xgboost running with tree_method = 'hist'

gistfile1.txt

import xgboost as xgb
import numpy as np
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split

rng = np.random.RandomState(1994)

digits = load_digits(2)
X = digits['data']
y = digits['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
dtrain = xgb.DMatrix(X_train, y_train)
dtest = xgb.DMatrix(X_test, y_test)
param = {'objective': 'binary:logistic',
        'tree_method': 'hist',
        'grow_policy': 'depthwise',
        'max_depth': 3,
        'eval_metric': 'auc'}
res = {}
bst = xgb.train(param, dtrain, 10, [(dtrain, 'train'), (dtest, 'test')],
        evals_result = res)