import xgboost
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

导入数据集：印度糖尿病病人

dataset = loadtxt('./data/pima-indians-diabetes.csv', delimiter=',')
#分为数据集和标签集
X = dataset[:,0:8]
Y = dataset[:,8]

分为训练集和测试集

seed = 6  #指定一个随机种子，保证每次分割都一致
test_size = 0.2 #训练集和验证集的分割比例
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)

训练Xgboost模型

model = XGBClassifier()

运行 XGBClassifier().fit()函数会使kernel直接挂掉，原因暂时未知，可能电脑有点老，内存不够？

model.fit(X_train,y_train)

做出预测和给出精确度

y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
print('Accuracy: %.2f%%' % (accuracy * 100.0))

---------------------------------------------------------------------------

XGBoostError                              Traceback (most recent call last)

<ipython-input-6-a4a337ba7f6f> in <module>
----> 1 y_pred = model.predict(X_test)
      2 predictions = [round(value) for value in y_pred]
      3 accuracy = accuracy_score(y_test, predictions)
      4 print('Accuracy: %.2f%%' % (accuracy * 100.0))


C:\ProgramData\Anaconda3\lib\site-packages\xgboost\sklearn.py in predict(self, data, output_margin, ntree_limit, validate_features)
    767         if ntree_limit is None:
    768             ntree_limit = getattr(self, "best_ntree_limit", 0)
--> 769         class_probs = self.get_booster().predict(test_dmatrix,
    770                                                  output_margin=output_margin,
    771                                                  ntree_limit=ntree_limit,


C:\ProgramData\Anaconda3\lib\site-packages\xgboost\sklearn.py in get_booster(self)
    185         """
    186         if self._Booster is None:
--> 187             raise XGBoostError('need to call fit or load_model beforehand')
    188         return self._Booster
    189 


XGBoostError: need to call fit or load_model beforehand