我在预测目标变量时遇到了以上错误。这个错误是什么意思?以及如何处理错误
data_set_train=pd.read_csv("train.csv")
data_set_testing=pd.read_csv("test.csv")
target_train_Y = data_set_train['target'].values
train_X = data_set_train.drop(['target','ID_code'],axis=1)
test_X = data_set_testing.drop(['ID_code'],axis=1)
sc=StandardScaler()
train=sc.fit_transform(train_X)
test=sc.transform(test_X)
xgb_prediction = []
K = 5
kf = KFold(n_splits = K,random_state = 3228,shuffle = True)
for train_index,test_index in kf.split(train):
train_X,valid_X = train[train_index],train[test_index]
train_y,valid_y = target_train_Y[train_index],target_train_Y[test_index]
xgb_params = {'max_depth': 8,'objective': 'binary:logistic','eval_metric':'auc'}
d_train = xgb.DMatrix(train_X,train_y)
d_valid = xgb.DMatrix(valid_X,valid_y)
d_test = xgb.DMatrix(test)
watchlist = [(d_train,'train'),(d_valid,'valid')]
model = xgb.train(xgb_params,d_train,500,watchlist,early_stopping_rounds=20)
xgb_pred = model.predict(d_test)
xgb_prediction.append(list(xgb_pred))
fig,ax = plt.subplots(1,1,figsize=(10,12))
xgb.plot_importance(model,max_num_features=30,ax=ax,importance_type="cover",xlabel="Cover")
print("---- drawing features by importance in descending order---")
plt.show()
preds=[]
for i in range(len(xgb_prediction[0])):
sum=0
for j in range(K):
sum+=xgb_prediction[j][i]
preds.append(sum / K)
错误:
IndexError
Traceback (most recent call last)
<ipython-input-17-70a24f3309e5> in <module>
3 sum=0
4 for j in range(K):
----> 5 sum+=xgb_prediction[j][i]
6 preds.append(sum / K)
IndexError: list index out of range