XGBoost Tenessee Eastman流程。错误：输入形状错误（980，​​2）

Question

我对Tenessee Eastmann流程中的XGBoost感到怀疑。我已经完成了数据预处理，当我拟合模型时，它显示了一些错误。 “错误的输入形状（980，​​2）” 而且如果我更改目标值并拟合模型，也没有问题，但是现在显示出另一个错误。 “分类指标不能同时处理连续目标和二进制目标” 有人熟悉Tenessee Eastman流程吗？

我的代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier, plot_tree
from sklearn.metrics import accuracy_score


Training_data = pd.read_excel ('X_train.xlsx')
Testing_data = pd.read_excel ('X_test.xlsx')
X_train, y_train = Training_data.iloc[:,:-1].values , Training_data.iloc[:,-1].values
X_test, y_test = Testing_data.iloc[:,:-1] , Testing_data.iloc[:,-1]

#from sklearn.preprocessing import OneHotEncoder
#enc = OneHotEncoder(categorical_features=[0]
#y_train_new = enc.fit_transform(y_train).toarray()

y_enc_train = pd.get_dummies(y_train)
y_enc_test = pd.get_dummies(y_test)


scaler = StandardScaler()
#training set
scaler.fit(X_train)
scaled_features_X_train = scaler.transform(X_train)
df_feat = pd.DataFrame(scaled_features_X_train)

#testing set
scaler.fit(X_test)
scaled_features_X_test = scaler.transform(X_test)
df_feat_test = pd.DataFrame(scaled_features_X_test)

#xgb model
classifier = XGBClassifier()
classifier.fit(df_feat,y_enc_train)
# make predictions for test data
y_pred = classifier.predict(df_feat_test)
predictions = [round(value) for value in y_pred]
# evaluate predictions
accuracy = accuracy_score(y_enc_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

错误：ValueError: bad input shape (980, 2)