如何实现后验概率分类数据？

Question

我是Python的初学者。我在实现后验概率时遇到问题。使用镜头数据集https://archive.ics.uci.edu/ml/datasets/Lenses。这是我的代码。

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

header=['age of the patient','spectacle prescription','astigmatic','tear production rate','target']
ds=pd.read_csv('lenses.data',names=header)

def Train_Test_Split_9(ds2):
    Y = ds2['target']
    X = ds2
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
    return X_train, X_test, Y_train, Y_test

X_train_9, X_test_9, Y_train, Y_test=Train_Test_Split_9(ds)
# =====================Posterior Probability================
def class_Probability(train_dataset):
    keys_class = np.array(train_dataset["target"].unique())
    p_class = np.zeros((len(keys_class)))
    for u in train_dataset["target"]:
        index_class = np.where(keys_class == u)
        p_class[index_class] += 1

    p_class=p_class/np.sum(p_class,axis=0)
    df_p_class=pd.DataFrame(p_class,keys_class)
    return df_p_class

p_class=class_Probability(X_train_9)

错误消息