利用yellowbrick绘制决策树残差图报错：ValueError: x and y must be the same size

Question

使用决策树网格搜索参数的找到最佳模型，但是想用yellowbrick画残差图的时候报错说X和y不是一个size，没办法绘制残差图，请问如何解决这个问题，部分代码如下：

import numpy as np
import matplotlib.pyplot as plt
import graphviz
from sqlalchemy import create_engine
from sklearn import tree,metrics
import pandas as pd
from yellowbrick.regressor import PredictionError,ResidualsPlot
from sklearn.model_selection import train_test_split,GridSearchCV
from yellowbrick.model_selection import LearningCurve,FeatureImportances
import matplotlib
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import Ridge
import seaborn as sns

datas = pd.read_sql_table(table_name,conn)

features = ['X1,'X2','X3',
        'X4,'X5']
target = ['Y']
X = np.array(datas[features])
y = np.array(datas[target])
X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.7,shuffle=True,random_state=0)

model = tree.DecisionTreeRegressor(criterion='squared_error'
                                  ,random_state= 0
                                  )
parameters = {'max_depth':[8,12],
              'min_samples_leaf':[1,3],
              'splitter':["best","random"],}
clf = GridSearchCV(estimator=model,param_grid=parameters)

clf = clf.fit(X_train,y_train)
print(clf.best_score_  )
print(clf.best_params_)
bestmodel = clf.best_estimator_
print(bestmodel)

ResPlot = ResidualsPlot(bestmodel)
ResPlot.fit(X_train, y_train)
ResPlot.score(X_test, y_test)
ResPlot.show()

ValueError: x and y must be the same size

搜寻过相关问题，我也用len()查了长度，训练集X，y都是87，测试集是38个数据，总共125个数据，也有说是格式不是arrary，但是我统一了数据格式在代码开头，我是5特征的数据，所以训练集的shape是875和871的。不知道哪里出的问题，麻烦解答。

绘制残差图