Python 属性错误：'；numpy.ndarray和#x27；对象没有属性'；得分'；错误

我试图寻找一个问题，但我认为这里没有什么问题。可能是什么？这是为了尝试在SVM中对时装MNIST数据集进行二元分类，但仅对5和7进行分类

import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn import svm
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import SVR
from sklearn.model_selection import KFold
import matplotlib.pyplot as plt

trainset = 'mnist_train.xlsx'
trs = pd.read_excel(trainset)
testset = 'mnist_test.xlsx'
tes = pd.read_excel(testset)
xtrain = trs.iloc[:, [1, 783]]
ytrain = trs.iloc[:, 0]
xtest = tes.iloc[:, [1, 783]]
ytest = tes.iloc[:, 0]


##Linear SVC

svclassifier = SVC(kernel='linear', C=1)
svclassifier.fit(xtest, ytest)
ypred = svclassifier.predict(xtest)
print(ypred.score(xtrain, ytrain))
print(ypred.score(xtest, ytest))

##Gaussian SVC

svclassifier = SVC(kernel='rbf', C=1)
svclassifier.fit(xtrain, ytrain)
ypred = svclassifier.predict(xtest)
print(ypred.score(xtrain, ytrain))
print(ypred.score(xtest, ytest))

---

ypred是一个预测类标签数组，因此异常是有意义的

您应该使用分类器的评分方法：

svclassifier = SVC(kernel='rbf', C=1)
svclassifier.fit(xtrain, ytrain)
# ypred = svclassifier.predict(xtest)  # We don’t actually use this. 
print(svclassifier.score(xtrain, ytrain))
print(svclassifier.score(xtest, ytest))

---

你试过打印ypred吗？谢谢你的清晰