我正在使用Kaggle - 心血管疾病數(shù)據(jù)集中的數(shù)據(jù)集。模型已經(jīng)過(guò)訓(xùn)練，我想做的是標(biāo)記以動(dòng)態(tài)方式插入的單個(gè)輸入（一行13個(gè)值）。數(shù)據(jù)集的形狀為 13 個(gè)特征 + 1 個(gè)目標(biāo)，66k 行#prepare dataset for train and testdfCardio = load_csv("cleanCardio.csv")y = dfCardio['cardio']x = dfCardio.drop('cardio',axis = 1, inplace=False)model = knn = KNeighborsClassifier()x_train,x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=42)model.fit(x_train, y_train)# make predictions for test datay_pred = model.predict(x_test)predictions = [round(value) for value in y_pred]# evaluate predictionsaccuracy = accuracy_score(y_test, predictions)print("Accuracy: %.2f%%" % (accuracy * 100.0))ML是訓(xùn)練的，我想做的是預(yù)測(cè)這一行的標(biāo)簽：['69','1','151','22','37','0','65','140','90','2','1','0','0','1']為目標(biāo)返回 0 或 1。所以我寫了這個(gè)代碼：import numpy as npimport pandas as pdsingle =  np.array(['69','1','151','22','37','0','65','140','90','2','1','0','0','1'])singledf = pd.DataFrame(single)final=singledf.transpose()prediction = model.predict(final)print(prediction)但它給出了錯(cuò)誤：查詢數(shù)據(jù)維度必須與訓(xùn)練數(shù)據(jù)維度匹配如何修復(fù)單行的標(biāo)簽？為什么我無(wú)法預(yù)測(cè)單個(gè)病例？