保留subject_ID并在 train_test_split 传递给没有 ID 列的模型数据帧之后，如下所示：

df.loc[ : , df.columns != 'subject_ID']

除非您在预测期间明确改组数据点，否则我相信通常返回的预测会保持初始顺序。绝对值得检查您正在使用的特定型号。

编辑：

请参阅下面的整个过程的示例：

import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split


iris = sns.load_dataset('iris')
# you don't need this, you have subject IDs.
iris['Subject_ID'] = numpy.random.randint(1, 10000, iris.shape[0])
# use subject IDs as index.
iris.set_index('Subject_ID', inplace=True)

# Split your data, index will be persisted.
Xtrain, Xtest, ytrain, ytest = train_test_split(
               iris.loc[ : , iris.columns != 'species'], 
               iris['species'],
               random_state=1
               )

# Model and predict.
from sklearn.naive_bayes import GaussianNB  # 1. choose model class
model = GaussianNB()                        # 2. instantiate model
model.fit(Xtrain, ytrain)                   # 3. fit model to data
y_hat = model.predict(Xtest, )              # 4. predict on new data

# Append predictions, original datapoint IDs will be encoded in the index.
Xtest["predictions"] = y_hat

# Test by joining original labels, using a dummy dataset for prediction.
Xtest = Xtest.join(iris['species'], how='inner')
Xtest.head()
>>>